Update README.md

README.md

@@ -2,38 +2,30 @@
 ---
 license: mit
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-# 0. FSL_ECG_QA_Dataset
+# 0. FSL_ECG_QA_Dataset Description
 
 **FSL_ECG_QA_Dataset** is a **benchmark dataset** specifically designed to accompany the paper *"Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning"* (**arXiv:2410.14464v1**). It supports research in combining **electrocardiogram (ECG) signals** with **natural language question answering (QA)**, particularly in **few-shot** and **meta-learning** scenarios.
 
-## 1. Dataset Highlights
+## 0.1 Supported Tasks
+
+- What tasks can be performed on this dataset?
+  Developing robust and reliable multimodal QA systems for ECG interpretation relies on the availability of both high-quality and large quantities of labeled data. Yet, obtaining massive amounts of labeled ECGs from cardiologists is costly, which often results in limited datasets. Traditional supervised learning methods tend to perform well only on data with the same distribution as the training data. In real-world deployment, however, models frequently encounter new tasks and previously unseen populations outside the training distribution, where traditional methods may fail.
+- Are there any code associated with this dataset?
+  Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning(ICASSP 2025), https://arxiv.org/html/2410.14464v1
+
+## 0.2 Dataset Highlights
 
 - 🧠 **Task Diversification**: Restructured **ECG-QA tasks** promote rapid **few-shot adaptation**.
 - 🧬 **Fusion Mapping**: A lightweight **multimodal mapper** bridges **ECG** and **language features**.
 - 🚀 **Model Generalization**: **LLM-agnostic design** ensures broad **transferability** and **robustness**.
 
-## 2 Developing Datasets
-### 2.1 Source Datasets
-
-The dataset is a structured reorganization of the existing ECG-QA dataset, adapted to suit meta-learning tasks. It draws samples from ECG sources such as [PTB-XL](https://physionet.org/content/ptb-xl/1.0.3/) and [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/1.0/), and [ECG-QA dataset](https://github.com/Jwoo5/ecg-qa?tab=readme-ov-file) organizes them into diverse task sets based on question types including **verify(yes/no)**, **choice(Condition_A/Condition_B)**, and **query(open-ended)** question in table 2.2. and clinical attributes (e.g., SCP codes, noise type, axis deviation) used to describing the ECG. This structure enables models to rapidly adapt to new diagnostic tasks with limited annotated examples.
-
-<img src="img/distribution_attr.png" alt="Figure 1: Illustration of class formation and attribute distribution for different question types." width="600"/>
-
-### 2.2 Dataset Description
+# 1 Developing Datasets
 
-Overview of question types and data distribution within the meta learning benchmark dataset created for few-shot ECG question answering.
+## 1.1 Source Datasets
 
-#### Supported Tasks
-
-- What tasks can be performed on this dataset?
-- Are there any code associated with this dataset?
-- Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning(ICASSP 2025), https://arxiv.org/html/2410.14464v1
-
-**class(template_id_attribute_answer pairs)**
-
-#### Data Instances
+The dataset is a structured reorganization of the existing ECG-QA dataset, adapted to suit meta-learning tasks. It draws samples from ECG sources such as [PTB-XL](https://physionet.org/content/ptb-xl/1.0.3/) and [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/1.0/), and [ECG-QA dataset](https://github.com/Jwoo5/ecg-qa?tab=readme-ov-file) organizes them into diverse task sets based on question types including **verify(yes/no)**, **choice(Condition_A/Condition_B)**, and **query(open-ended)** question in table 2.2. and clinical attributes (e.g., SCP codes, noise type, axis deviation) used to describing the ECG. This structure enables models to rapidly adapt to new diagnostic tasks with limited annotated examples.
 
-Merge all data in train/val/test dataset in ECG-QA and change it into different class, Use a consistent ID system to track attribute-answer combinations (definition of "way" in meta-learning):
+### 1.1.1 Source Datasets Instances
 
 ```python
 {
@@ -48,12 +40,26 @@ Merge all data in train/val/test dataset in ECG-QA and change it into different
     "attribute": ["non-diagnostic t abnormalities"]
 }
 ```
+### 1.1.2 Source Datasets Instances distribution
 
-#### New Class Naming
+<img src="img/distribution_attr.png" alt="Figure 1: Illustration of class formation and attribute distribution for different question types." width="600"/>
+
+## 1.2 New few shot learning Datasets
 
+### 1.2.1 New Class Naming
+Merge all data in train/val/test dataset in ECG-QA and change it into different class, Use a consistent ID system to track attribute-answer combinations (definition of "way" in meta-learning):
+**class(template_id_attribute_answer pairs)**
 Example: `5_atrial_fibrillation_yes` represents Template ID *5*, attribute *"atrial fibrillation"*, answer *"yes"*.
 
-#### Class Build
+### 1.2.2 Loading the Dataset class
+
+```python
+python load_class.py --base_path /your/actual/path/to/ecgqa/ptbxl/paraphrased --test_dataset ptb-xl
+python load_class.py --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased --test_dataset mimic
+(all tested mimic-iv-ecg dataset is listed in "data/processed_test_30k.json")
+```
+
+### 1.2.3 New Datasets Class distribution
 
 | Question Type   | Attributes | Answers                          | Classes (train:test) | Samples | Example                                                                 |
 |-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
@@ -62,12 +68,8 @@ Example: `5_atrial_fibrillation_yes` represents Template ID *5*, attribute *"atr
 | Single-Query    | 30         | attr_1/attr_2/.../attr_n         | 260 (208:52)          | 63,125  | Q: What direction is this ECG deviated to? <br> A: Normal axis/...     |
 | All             | 206        | yes/no/both/none/.../attr_n      | 678 (541:137)         | 144,885 | ...                                                                     |
 
-#### Loading the Dataset
+### Few-shot Build
 
-```bash
-python load_class.py --base_path /your/actual/path/to/ecgqa/ptbxl/paraphrased --test_dataset ptb-xl
-python load_class.py --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased --test_dataset mimic
-```
 
 ```python
 train_data = dataset["train"]
@@ -78,8 +80,6 @@ for example in train_data.select(range(3)):
     print(example)
 ```
 
-### Few-shot Build
-
 ```bash
 python data_loader.py --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased --test_dataset mimic
 ```