A common problem in medical diagnosis is to combine information from several tests or patient characteristics into a decision rule to distinguish diseased from healthy patients . Among the statistical procedures proposed to solve this problem , recursive partitioning is appealing for the easily-used and intuitive nature of the rules it produces . The CAD is a relatively young interdisciplinary technology , has had a tremendous impact on medical diagnosis specifically cancer detection . The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm . Therefore , in order to meet the requirements that arise , our R&D team has created an environment where developers can test different approaches in data processing . To assist the objective , the platform offers a number of features with the following main target goals : 1 ) to increase the effectiveness in conducting a proper medical diagnose , 2 ) to incorporate a unified format of storing the results of the diagnosis conducted and 3 ) to test various ECG QRS detection algorithms . In this paper we present a multimethod approach for induction of a specific class of classifiers , which can assist physicians in medical diagnosing in the case of mitral valve prolapse . Mitral valve prolapse is one of the most controversial prevalent cardiac condition and may affect up to ten percent of the population and in the worst case results in sudden death . Adaptive filtering method can realize effective extraction of non-stationary signals without knowing a priori knowledge about signal and noise , this paper presents an adaptive noise cancellation system for ECG signal base line filtering and power interference suppression , constructs an iterative time LMS algorithm combining variable and fixed step size , which effectively solves the problems of filtering SNR and convergence rate . The experiment results show that this method improves 26.36dB in SNR , eliminates base line drift and power interference effectively , extracts ECG signal accurately and converges quickly , has important practical value in medical clinical diagnosis . On the basis of building an adaptive noise cancellation system for suppressing power interference and base line drift from ECG signal , this paper puts forward R wave detecting algorithm based on coif5 wavelet , adopts difference smoothing method to realize Q wave and S wave detecting and positioning . The experiment results show that the adaptive filter and QRS complex detecting algorithms presented in this paper suppress power interference and base line drift effectively , reach the aim of ECG signal QRS waveform detection and accurate positioning and have an important practical value in medical clinical diagnosis . Wireless body area networks ( WBANs ) rely on powerful cloud storage services to manage massive medical data . As precise medical diagnosis analysis is heavily based on these medical data , any altered medical data may cause severe consequences , the integrity of outsourced medical data has become the most concerning security issue . Up to date , most existing public auditing mechanisms have been proposed to check the data integrity , but they could not achieve conditional identity privacy , any patient would not like others to know his/her real identity corresponding to certain serious disease , and some malicious patients should be revoked timely due to misbehaviors . Besides , the multifunctional vagina cervix detector can be connected with a mobile telephone , the tablet personal computer , the electronic computer and a television so that the female can conduct checking on a vagina cavity and a cervix part personally at home . Medical indexes of the relevant positions can be collected for medical diagnosis analysis by means of the sensors according to actual requirements , and early detection of vagina and cervix diseases is ensured . Recent developments in Artificial intelligence and their applications in Medical health diagnosis have shown promising results especially in the diagnosis of brain disorders , thereby paving the way for better healthcare and early detection of Neural disorders . Several diagnostic tests are routinely performed on humans to evaluate the nature of brain signals and images . The diagnosis accuracy is a critical issue to the successful implementation of the medical decision support systems . In order to improve the decision accuracy in medical health diagnosis , the problems associated with uncertainty and fuzziness are of great concern in medical decision-making . This paper is mainly concerned with handling fuzzy and uncertain features in health diagnosis . This is a tremendous accomplishment in a relatively short span of time thanks to Prof. Akyildiz ’s outstanding leadership and dedication , and sustained contributions of authors , editors , reviewers and Elsevier ’s staff members . Information and communication technologies span every aspect of our lives with applications ranging from digital telephony , wireless cellular networks , medical diagnosis/treatment to sensor networks , deep-space communications , etc . Growing importance of ICT are also recognized by the funding agencies across the world , for instance , through the Horizon 2020 program of European Commission , recently completed Information Technology Research ( ITR ) program and the newly announced Advanced Wireless Research Initiative of the United States National Science Foundation ( NSF ) . Medical modern devices in imaging have supported medical patient diagnosis . The important medical information could not naturally visible . However , the last row shows another type of annotation error . Contrast media is a general term for a medium used in medical imaging and since is not a proper noun , it is not a named entity . These examples shows the presence of incorrect ground truth labels , which can harm the performance of bioNER models . Shallower layers grasp local information while deeper layers use filters whose receptive fields are much broader that therefore capture global information . Segmentation is a highly relevant task in medical image analysis . Automatic delineation of organs and structures of interest is often necessary to perform tasks such as visual augmentation , computer assisted diagnosis , interventions and extraction of quantitative indices from images . Prostate segmentation is nevertheless an important task having clinical relevance both during diagnosis , where the volume of the prostate needs to be assessed , and during treatment planning , where the estimate of the anatomical boundary needs to be accurate . CNNs have been recently used for medical image segmentation . Early approaches obtain anatomy delineation in images or volumes by performing patch - wise image classification . section : Introduction In 2015 alone , about 100 manuscripts describing randomized controlled trials ( RCTs ) for medical interventions were published every day . It is thus practically impossible for physicians to know which is the best medical intervention for a given patient group and condition . Blood cell analysis , including blood cell counting , is the key point for modern pathological study as well as medical diagnosis . Taking into account both resources and environment of the medical research , analyzing blood cells under the microscope , instead of dedicated blood cell analyzer , provides a more intuitive and convenient way for research uses . Objective To establish a network laboratory for blood cell analysis and better calibrate haematology analyzers in local lab . Methods According to GB/T 15481《General requirements for the competence testing and calibration laboratories》(idt ISO/IEC 17025),we established a network laboratory providing traceability for blood cell analysis . Complete blood count was traced to Calibration Laboratory in NCCL;The secondary standard haematology analyzer with the same model and calibrator with same lot number were used for verification for a long period . The aim of this research is to produce a survey on computer vision system that can detect and estimate the number of red blood cells in the blood sample image using image processing algorithms . This paper considers image processing for counting of blood cells . Image processing algorithms involve six major steps : image acquisition , preprocessing , image enhancement , image segmentation , feature extraction and counting algorithm . To prove functionality of our program , we used it in real-time closed loop control of hydrodynamic focusing and control of flow velocity of cells inside a microchannel . We also demonstrated the program abilities in different lab-on-chip applications such as tracking , differentiation , and counting of blood cells . Moreover , an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform , local maxima , and connected component labeling . The counting result of the proposed procedure achieves an accuracy of 93.3 % for red blood cell count using dataset 1 and 97.38 % for white blood cell count using dataset 2 . We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets . As a proof of concept , we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count , multiple sclerosis , celiac disease and HDL cholesterol . Availability and implementation : http://broadinstitute.org/mpg/snpsea Contact : soumya@broadinstitute.org Supplementary information : Supplementary data are available at Bioinformatics online . Despite the widespread use of exogenous markers for blood cell quantification , label-free optical methods are still of high demand due to their possibility for in vivo application and signal specific to the biochemical state of the cell provided by native fluorophores . Here we report the results of blood cell characterization using label-free fluorescence imaging techniques and flow-cytometry . Autofluorescence parameters of different cell types - white blood cells , red blood cells , erythrophagocytic cells - are assessed and analyzed in terms of molecular heterogeneity and possibilities of differentiation between different cell types in vitro and in vivo . We present a visualization pipeline from sample to answer for point-of-care blood cell counting applications . Effective and low-cost point-of-care medical diagnostic tests provide developing countries and rural communities with accessible healthcare solutions [ 1 ] , and can be particularly beneficial for blood cell count tests , which are often the starting point in the process of diagnosing a patient [ 2 ] . The initial focus of this work is on total white and red blood cell counts , using a microfluidic cartridge [ 3 ] for sample processing . Microscopic image processing is critical aspects to biomedical image analysis , and blood cell counts are very important role in medical diagnoses . Various dyeing methods and microscopes are used , so we need methods that can effectively count cells by adapting to such diversity . Different filtering approaches perform better depending on the SNR of PA images . For the blood cell study , based on the standard deviation in the background , sym7/sqtwolog/soft thresh . combination provided the best improvement and this result supported the experimental phantom results . The Sysmex NE 8000 ( TOA-Japan ) is a haematology analyser that performs blood cells count and leukocyte differential count . For facilitating the work of technical validation , we developed a software adapted to any IBM or compatible PC running under MS-DOS , to manage the analyser . Thoracic diseases are serious health problems that plague a significant amount of people . Chest X-ray is currently one of the most popular methods to diagnose thoracic diseases and plays an important role in the healthcare workflow . In this paper , we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network ( FHRNet ) . The FHRNet concatenates the global average pooling layers of the global and local feature extractors — it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification . Compared with the results of other available methods , our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset , according to the receiver operating characteristic curve and area-under-the-curve score . Superior to existing consistency-based methods which simply enforce consistency of individual predictions , our framework explicitly enforces the consistency of semantic relation among different samples under perturbations , encouraging the model to explore extra semantic information from unlabeled data . We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets , i.e.,skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14 . Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios . Thoracic disorders , such as lung and liver cancer , and congestive heart failure are the major causes of chronic morbidity and mortality in the world . Electromagnetic imaging ( EMI ) is an emerging technique to diagnose those abnormalities in a fast and cost-effective way . The aims of such aids being to improve accuracy , consistency and cost-effectiveness of clinical decisions . The program described in this paper deals with the diagnosis of thoracic disorders in patients whose chest X-ray is substantially normal and who are complaining of one or more of the symptoms cough , shortness of breath , coughing up blood or chest pain . Standard observations such as the patient 's age , height and weight and the responses to a few more detailed questions about the patient 's presenting symptoms are recorded . Computed Tomography ( CT ) of the lungs play a key role in clinical investigation of thoracic malignancies , as well as having the potential to increase our knowledge about pulmonary diseases including cancer . It enables longitudinal trials to monitor lung disease progression , and to inform assessment of lung damage resulting from radiation therapy . However , this finding is preliminary and warrants further investigation . ConclusionThough the database project is still under development , the application of such a database has the potential to enhance our understanding of cancer biology and will help researchers to identify targets to modify the course of thoracic malignancies . PURPOSE The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues . Intensity modulated radiation therapy ( IMRT ) planning is a lengthy process , which requires the planner to iterate between choosing beam angles , specifying dose-volume objectives and executing IMRT optimization . Practical implications : Researchers may want to focus on creating models segregated by demographics and pre-process the modeling images using image processing filters . Practitioners developing assistive technologies for thoracic diagnoses may benefit from incorporating demographics and employing multiple models simultaneously with varying statistical likelihood . Originality/value : This study uses demographics in model creation and utilizes image processing filters to improve model performance . This task remains a major challenge due to 1 ) the shortage of accurate annotations for chest X-ray examinations , 2 ) the diversity of lesion areas on X-rays from different thoracic disease and 3 ) the problem of class imbalance in existing chest X-ray databases . In this paper , we propose a new multi-attention convolutional neural network for thoracic disease classification and localization . First , the framework is equipped with squeeze-and-excitation ( SE ) block as a feature attention module to offer a chance of cross-channel feature recalibration . Weakly supervised learning is capable of achieving fine-grained tasks with coarse annotations , which has shown great potential in computer-aided diagnosis . This study aims to achieve thoracic disease diagnosis in a weakly supervised manner only with coarse image-level annotations . Except for considering the performance of disease diagnosis , the study concentrates more on discovering the location of the pathological area which is used as visualised evidence for interpretability of diagnosis and the following retrospective analysis . Given image labels as the only supervisory signal , we focus on harvesting/mining , thoracic disease localizations from chest X-ray images . Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses . Compared to existing manual inspection solutions by surveyors , this method is more suitable for inspecting large areas by means of capturing and analyzing pictures/videos of the target areas . In this paper , a MAV facilitated CBC assessment system implementing deep learning for object recognition has been developed to provide effective CBC assessment for marine and offshore industries . By using active thermography , it is able to identify corrosion behind coatings . The results should help C-Test users make informed decisions when embarking on using C-Test for their next testing project . It should also be useful to language testing researchers who want to investigate some ripe areas of research in C-Test . This is a further argument that the C-test format is applicable in a large variety of structurally different languages . The fact that University Language Testing and the C-test is part of the " reading recommendations " of the Academic Unit ( Languages ) of the Open University UK is an indication of the impact of this ( and other ) chapters published in this book . A common technique for multiple string search uses an index of hashes , referred to as fingerprints , of strings sampled from the text , which are then compared with hashes of substrings from the search string . In this thesis , we investigate the suitability of this technique to the problem of CBC . A CBC system , cobald , was developed which employs a two-step scheme : a preliminary long-range delta encoding step using the fingerprint index , followed by a compression of the delta file by a standard compression utility . Fractional factorial design was applied to both the “ one-dimensional ” features and the “ attractive ” features for individual CBCs . Random sequences of the combinations of attribute levels were generated for each of the three types of CBC analyses ( “ must-be ” , “ one-dimensional ” and “ attractive ” ) . At the same time , the fourth step conducted a survey of the individual groups for the conjoint analysis on the functional characteristics of a mobile phone . A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories . Generative Adversarial Networks ( GANs ) represent a promising class of generative networks that combine neural networks with game theory . The technique of the antiblastogram is described . The antiblastogram is used for the in vitro study of anti-cancer chemotherapy agents and predictive drug testing . The current state of the art and potential applications of self-organized cell-based systems , organ-on-a-chip devices that incorporate sensors and monitoring capabilities , as well as microfabrication techniques including bioprinting and photolithography , are discussed . These techniques could be combined to help provide highly predictive drug tests for patient-specific conditions in the near future .
A common problem in medical diagnosis is to combine information from several tests or patient characteristics into a decision rule to distinguish diseased from healthy patients . Among the statistical procedures proposed to solve this problem , recursive partitioning is appealing for the easily-used and intuitive nature of the rules it produces .
The CAD is a relatively young interdisciplinary technology , has had a tremendous impact on medical diagnosis specifically cancer detection . The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm .
Therefore , in order to meet the requirements that arise , our R&D team has created an environment where developers can test different approaches in data processing . To assist the objective , the platform offers a number of features with the following main target goals : 1 ) to increase the effectiveness in conducting a proper medical diagnose , 2 ) to incorporate a unified format of storing the results of the diagnosis conducted and 3 ) to test various ECG QRS detection algorithms .
In this paper we present a multimethod approach for induction of a specific class of classifiers , which can assist physicians in medical diagnosing in the case of mitral valve prolapse . Mitral valve prolapse is one of the most controversial prevalent cardiac condition and may affect up to ten percent of the population and in the worst case results in sudden death .
Adaptive filtering method can realize effective extraction of non-stationary signals without knowing a priori knowledge about signal and noise , this paper presents an adaptive noise cancellation system for ECG signal base line filtering and power interference suppression , constructs an iterative time LMS algorithm combining variable and fixed step size , which effectively solves the problems of filtering SNR and convergence rate . The experiment results show that this method improves 26.36dB in SNR , eliminates base line drift and power interference effectively , extracts ECG signal accurately and converges quickly , has important practical value in medical clinical diagnosis .
On the basis of building an adaptive noise cancellation system for suppressing power interference and base line drift from ECG signal , this paper puts forward R wave detecting algorithm based on coif5 wavelet , adopts difference smoothing method to realize Q wave and S wave detecting and positioning . The experiment results show that the adaptive filter and QRS complex detecting algorithms presented in this paper suppress power interference and base line drift effectively , reach the aim of ECG signal QRS waveform detection and accurate positioning and have an important practical value in medical clinical diagnosis .
Wireless body area networks ( WBANs ) rely on powerful cloud storage services to manage massive medical data . As precise medical diagnosis analysis is heavily based on these medical data , any altered medical data may cause severe consequences , the integrity of outsourced medical data has become the most concerning security issue . Up to date , most existing public auditing mechanisms have been proposed to check the data integrity , but they could not achieve conditional identity privacy , any patient would not like others to know his/her real identity corresponding to certain serious disease , and some malicious patients should be revoked timely due to misbehaviors .
Besides , the multifunctional vagina cervix detector can be connected with a mobile telephone , the tablet personal computer , the electronic computer and a television so that the female can conduct checking on a vagina cavity and a cervix part personally at home . Medical indexes of the relevant positions can be collected for medical diagnosis analysis by means of the sensors according to actual requirements , and early detection of vagina and cervix diseases is ensured .
Recent developments in Artificial intelligence and their applications in Medical health diagnosis have shown promising results especially in the diagnosis of brain disorders , thereby paving the way for better healthcare and early detection of Neural disorders . Several diagnostic tests are routinely performed on humans to evaluate the nature of brain signals and images .
The diagnosis accuracy is a critical issue to the successful implementation of the medical decision support systems . In order to improve the decision accuracy in medical health diagnosis , the problems associated with uncertainty and fuzziness are of great concern in medical decision-making . This paper is mainly concerned with handling fuzzy and uncertain features in health diagnosis .
This is a tremendous accomplishment in a relatively short span of time thanks to Prof. Akyildiz ’s outstanding leadership and dedication , and sustained contributions of authors , editors , reviewers and Elsevier ’s staff members . Information and communication technologies span every aspect of our lives with applications ranging from digital telephony , wireless cellular networks , medical diagnosis/treatment to sensor networks , deep-space communications , etc . Growing importance of ICT are also recognized by the funding agencies across the world , for instance , through the Horizon 2020 program of European Commission , recently completed Information Technology Research ( ITR ) program and the newly announced Advanced Wireless Research Initiative of the United States National Science Foundation ( NSF ) .
Medical modern devices in imaging have supported medical patient diagnosis . The important medical information could not naturally visible .
However , the last row shows another type of annotation error . Contrast media is a general term for a medium used in medical imaging and since is not a proper noun , it is not a named entity . These examples shows the presence of incorrect ground truth labels , which can harm the performance of bioNER models .
Shallower layers grasp local information while deeper layers use filters whose receptive fields are much broader that therefore capture global information . Segmentation is a highly relevant task in medical image analysis . Automatic delineation of organs and structures of interest is often necessary to perform tasks such as visual augmentation , computer assisted diagnosis , interventions and extraction of quantitative indices from images .
Prostate segmentation is nevertheless an important task having clinical relevance both during diagnosis , where the volume of the prostate needs to be assessed , and during treatment planning , where the estimate of the anatomical boundary needs to be accurate . CNNs have been recently used for medical image segmentation . Early approaches obtain anatomy delineation in images or volumes by performing patch - wise image classification .
section : Introduction In 2015 alone , about 100 manuscripts describing randomized controlled trials ( RCTs ) for medical interventions were published every day . It is thus practically impossible for physicians to know which is the best medical intervention for a given patient group and condition .
Blood cell analysis , including blood cell counting , is the key point for modern pathological study as well as medical diagnosis . Taking into account both resources and environment of the medical research , analyzing blood cells under the microscope , instead of dedicated blood cell analyzer , provides a more intuitive and convenient way for research uses .
Objective To establish a network laboratory for blood cell analysis and better calibrate haematology analyzers in local lab . Methods According to GB/T 15481《General requirements for the competence testing and calibration laboratories》(idt ISO/IEC 17025),we established a network laboratory providing traceability for blood cell analysis . Complete blood count was traced to Calibration Laboratory in NCCL;The secondary standard haematology analyzer with the same model and calibrator with same lot number were used for verification for a long period .
The aim of this research is to produce a survey on computer vision system that can detect and estimate the number of red blood cells in the blood sample image using image processing algorithms . This paper considers image processing for counting of blood cells . Image processing algorithms involve six major steps : image acquisition , preprocessing , image enhancement , image segmentation , feature extraction and counting algorithm .
To prove functionality of our program , we used it in real-time closed loop control of hydrodynamic focusing and control of flow velocity of cells inside a microchannel . We also demonstrated the program abilities in different lab-on-chip applications such as tracking , differentiation , and counting of blood cells .
Moreover , an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform , local maxima , and connected component labeling . The counting result of the proposed procedure achieves an accuracy of 93.3 % for red blood cell count using dataset 1 and 97.38 % for white blood cell count using dataset 2 .
We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets . As a proof of concept , we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count , multiple sclerosis , celiac disease and HDL cholesterol . Availability and implementation : http://broadinstitute.org/mpg/snpsea Contact : soumya@broadinstitute.org Supplementary information : Supplementary data are available at Bioinformatics online .
Despite the widespread use of exogenous markers for blood cell quantification , label-free optical methods are still of high demand due to their possibility for in vivo application and signal specific to the biochemical state of the cell provided by native fluorophores . Here we report the results of blood cell characterization using label-free fluorescence imaging techniques and flow-cytometry . Autofluorescence parameters of different cell types - white blood cells , red blood cells , erythrophagocytic cells - are assessed and analyzed in terms of molecular heterogeneity and possibilities of differentiation between different cell types in vitro and in vivo .
We present a visualization pipeline from sample to answer for point-of-care blood cell counting applications . Effective and low-cost point-of-care medical diagnostic tests provide developing countries and rural communities with accessible healthcare solutions [ 1 ] , and can be particularly beneficial for blood cell count tests , which are often the starting point in the process of diagnosing a patient [ 2 ] . The initial focus of this work is on total white and red blood cell counts , using a microfluidic cartridge [ 3 ] for sample processing .
Microscopic image processing is critical aspects to biomedical image analysis , and blood cell counts are very important role in medical diagnoses . Various dyeing methods and microscopes are used , so we need methods that can effectively count cells by adapting to such diversity .
Different filtering approaches perform better depending on the SNR of PA images . For the blood cell study , based on the standard deviation in the background , sym7/sqtwolog/soft thresh . combination provided the best improvement and this result supported the experimental phantom results .
The Sysmex NE 8000 ( TOA-Japan ) is a haematology analyser that performs blood cells count and leukocyte differential count . For facilitating the work of technical validation , we developed a software adapted to any IBM or compatible PC running under MS-DOS , to manage the analyser .
Thoracic diseases are serious health problems that plague a significant amount of people . Chest X-ray is currently one of the most popular methods to diagnose thoracic diseases and plays an important role in the healthcare workflow .
In this paper , we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network ( FHRNet ) . The FHRNet concatenates the global average pooling layers of the global and local feature extractors — it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification . Compared with the results of other available methods , our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset , according to the receiver operating characteristic curve and area-under-the-curve score .
Superior to existing consistency-based methods which simply enforce consistency of individual predictions , our framework explicitly enforces the consistency of semantic relation among different samples under perturbations , encouraging the model to explore extra semantic information from unlabeled data . We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets , i.e.,skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14 . Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios .
Thoracic disorders , such as lung and liver cancer , and congestive heart failure are the major causes of chronic morbidity and mortality in the world . Electromagnetic imaging ( EMI ) is an emerging technique to diagnose those abnormalities in a fast and cost-effective way .
The aims of such aids being to improve accuracy , consistency and cost-effectiveness of clinical decisions . The program described in this paper deals with the diagnosis of thoracic disorders in patients whose chest X-ray is substantially normal and who are complaining of one or more of the symptoms cough , shortness of breath , coughing up blood or chest pain . Standard observations such as the patient 's age , height and weight and the responses to a few more detailed questions about the patient 's presenting symptoms are recorded .
Computed Tomography ( CT ) of the lungs play a key role in clinical investigation of thoracic malignancies , as well as having the potential to increase our knowledge about pulmonary diseases including cancer . It enables longitudinal trials to monitor lung disease progression , and to inform assessment of lung damage resulting from radiation therapy .
However , this finding is preliminary and warrants further investigation . ConclusionThough the database project is still under development , the application of such a database has the potential to enhance our understanding of cancer biology and will help researchers to identify targets to modify the course of thoracic malignancies .
PURPOSE The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues . Intensity modulated radiation therapy ( IMRT ) planning is a lengthy process , which requires the planner to iterate between choosing beam angles , specifying dose-volume objectives and executing IMRT optimization .
Practical implications : Researchers may want to focus on creating models segregated by demographics and pre-process the modeling images using image processing filters . Practitioners developing assistive technologies for thoracic diagnoses may benefit from incorporating demographics and employing multiple models simultaneously with varying statistical likelihood . Originality/value : This study uses demographics in model creation and utilizes image processing filters to improve model performance .
This task remains a major challenge due to 1 ) the shortage of accurate annotations for chest X-ray examinations , 2 ) the diversity of lesion areas on X-rays from different thoracic disease and 3 ) the problem of class imbalance in existing chest X-ray databases . In this paper , we propose a new multi-attention convolutional neural network for thoracic disease classification and localization . First , the framework is equipped with squeeze-and-excitation ( SE ) block as a feature attention module to offer a chance of cross-channel feature recalibration .
Weakly supervised learning is capable of achieving fine-grained tasks with coarse annotations , which has shown great potential in computer-aided diagnosis . This study aims to achieve thoracic disease diagnosis in a weakly supervised manner only with coarse image-level annotations . Except for considering the performance of disease diagnosis , the study concentrates more on discovering the location of the pathological area which is used as visualised evidence for interpretability of diagnosis and the following retrospective analysis .
Given image labels as the only supervisory signal , we focus on harvesting/mining , thoracic disease localizations from chest X-ray images . Harvesting such localizations from existing datasets allows for the creation of improved data sources for computer-aided diagnosis and retrospective analyses .
Compared to existing manual inspection solutions by surveyors , this method is more suitable for inspecting large areas by means of capturing and analyzing pictures/videos of the target areas . In this paper , a MAV facilitated CBC assessment system implementing deep learning for object recognition has been developed to provide effective CBC assessment for marine and offshore industries . By using active thermography , it is able to identify corrosion behind coatings .
The results should help C-Test users make informed decisions when embarking on using C-Test for their next testing project . It should also be useful to language testing researchers who want to investigate some ripe areas of research in C-Test .
This is a further argument that the C-test format is applicable in a large variety of structurally different languages . The fact that University Language Testing and the C-test is part of the " reading recommendations " of the Academic Unit ( Languages ) of the Open University UK is an indication of the impact of this ( and other ) chapters published in this book .
A common technique for multiple string search uses an index of hashes , referred to as fingerprints , of strings sampled from the text , which are then compared with hashes of substrings from the search string . In this thesis , we investigate the suitability of this technique to the problem of CBC . A CBC system , cobald , was developed which employs a two-step scheme : a preliminary long-range delta encoding step using the fingerprint index , followed by a compression of the delta file by a standard compression utility .
Fractional factorial design was applied to both the “ one-dimensional ” features and the “ attractive ” features for individual CBCs . Random sequences of the combinations of attribute levels were generated for each of the three types of CBC analyses ( “ must-be ” , “ one-dimensional ” and “ attractive ” ) . At the same time , the fourth step conducted a survey of the individual groups for the conjoint analysis on the functional characteristics of a mobile phone .
A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories . Generative Adversarial Networks ( GANs ) represent a promising class of generative networks that combine neural networks with game theory .
The technique of the antiblastogram is described . The antiblastogram is used for the in vitro study of anti-cancer chemotherapy agents and predictive drug testing .
The current state of the art and potential applications of self-organized cell-based systems , organ-on-a-chip devices that incorporate sensors and monitoring capabilities , as well as microfabrication techniques including bioprinting and photolithography , are discussed . These techniques could be combined to help provide highly predictive drug tests for patient-specific conditions in the near future .
ROC Curves for Classification Trees Adaptive Edge Detection Method towards Features Extraction from Diverse Medical Imaging Technologies Visualization in the ECG QRS detection algorithms Multimethod machine learning approach for medical diagnosing ECG signal base line filtering and power interference suppression method ECG signal adaptive filtering and QRS complex detecting method CIPPPA: Conditional Identity Privacy-Preserving Public Auditing for Cloud-Based WBANs against Malicious Auditors Multifunctional vagina cervix detector Contribution of Application of Deep Learning Approaches on Biomedical Data in the Diagnosis of Neurological Disorders: A Review on Recent Findings A Fuzzy Inference Approach for Health Self-Diagnosis under Uncertainty Editorial: Receiving the baton IDENTIFIKASI CITRA OTAK MRIBERBASIS LOGIKA FUZZY TEROPTIMASIDAN EIGENBRAIN IMAGE CollaboNet: collaboration of deep neural networks for biomedical named entity recognition V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature An automatic red blood cell counting method based on spectral images Establishment of network laboratory for blood cell analysis and its quality assurance Survey on Automatic RBC Detection andCounting Multi-purpose machine vision platform for different microfluidics applications Blood Cell Count Using Deep Learning Semantic Segmentation SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci Label-free characterization of white blood cells using fluorescence lifetime imaging and flow-cytometry: molecular heterogeneity and erythrophagocytosis [Invited]. Sample to answer visualization pipeline for low-cost point-of-care blood cell counting A self-adapting method for RBC count from different blood smears based on PCNN and image quality Comparison of noise reduction methods in photoacoustic microscopy A data management software for the Sysmex NE 8000 haematology analyser. Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images Fusion High-Resolution Network for Diagnosing ChestX-ray Images Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model Three-Dimensional Electromagnetic Torso Imaging Using Reconfigurable Antennas Early experiences with a program designed for the diagnosis of thoracic disorders Mass Transportation for Deformable Image Registration with Application to Lung CT Proteomic characterization of non-small cell lung cancer in a comprehensive translational thoracic oncology database Automatic learning-based beam angle selection for thoracic IMRT. Convolutional neural networks model improvements using demographics and image processing filters on chest x-rays Multi-attention Network for Thoracic Disease Classification and Localization Spatial non-local attention for thoracic disease diagnosis and visualisation in weakly supervised learning Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays An Integrated Coating Inspection System for Marine and Offshore Corrosion Management C-Test Construct Validation The development of a Turkish C-Test String hashing for collection-based compression Measuring willingness-to-pay for mobile phone features: a multi-region study Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories [The antiblastogram technic: application possibilities and limitations]. Mimicking Epithelial Tissues in Three-Dimensional Cell Culture Models
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MIPRO BMC Bioinformatics 2016 Fourth International Conference on 3D Vision (3DV) ACL CISP-BMEI BIBM BIBM CMMI/RAMBO/SWITCH@MICCAI ICASSP MICCAI (2) ICARCV
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