Teacher:You are given a paper review either in Spanish or English language. Based on the given review, evaluate the paper by classifying it to one of the five classes: (1) very negative, (2) negative, (3) neutral, (4) positive, and (5) very positive. Note that URLs in the text have been replaced with [Link].
Teacher: Now, understand the problem? Solve this instance: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification.  The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written.  - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough.  - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.
Student:
positive