Update README.md

README.md

@@ -4,16 +4,7 @@ license: mit
 ---
 # 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.
-
-## 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
+**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"*] (https://arxiv.org/abs/2410.14464). It supports research in combining **electrocardiogram (ECG) signals** with **natural language question answering (QA)**, particularly in **few-shot** and **meta-learning** scenarios.
 
 ## 0.2 Dataset Highlights
 
@@ -21,66 +12,16 @@ license: mit
 - 🧬 **Fusion Mapping**: A lightweight **multimodal mapper** bridges **ECG** and **language features**.
 - 🚀 **Model Generalization**: **LLM-agnostic design** ensures broad **transferability** and **robustness**.
 
-# 1 Developing Datasets
-
-## 1.1 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.
-
-| Task Type       | Question                                                                 | Answer Options                          |
-|-----------------|--------------------------------------------------------------------------|------------------------------------------|
-| Single-Verify   | Q: Does this ECG show 1st degree AV block?                               | A: yes / no                              |
-| Single-Choose   | Q: Which noise does this ECG show, baseline drift or static noise?       | A: baseline drift / static noise         |
-| Single-Query    | Q: What direction is this ECG deviated to?                               | A: Normal axis / ...                     |
-
-
-### 1.1.1 Source Datasets Instances
-
-```python
-{
-    "template_id": 1,
-    "question_id": 0,
-    "sample_id": 0,
-    "question_type": "single-verify",
-    "attribute_type": "scp_code",
-    "question": "Is there evidence of non-diagnostic t abnormalities on this ECG?",
-    "answer": ["yes"],
-    "ecg_id": [12662],
-    "attribute": ["non-diagnostic t abnormalities"]
-}
-```
-
-### 1.1.2 Source Datasets Instances distribution
-
-<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 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"*.
-
-### 1.2.2 Loading the Dataset class
+## 1.1 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                                                                 |
-|-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
-| Single-Verify   | 94         | yes/no                           | 156 (124:32)          | 34,105  | Q: Does this ECG show 1st degree av block? <br> A: yes/no               |
-| Single-Choose   | 165        | both/none/attr_1/attr_2          | 262 (209:53)          | 47,655  | Q: Which noise does this ECG show, baseline drift or static noise? <br> A:  baseline drift /static noise   |
-| 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 | ...                                                                     |
-
-## 1.3 Few-shot Build
-
-### 1.3.1 load ECG-QA-FSL dataset
+## 1.2 load ECG-QA-FSL dataset
 
 ```python
 python data_loader.py
@@ -88,8 +29,8 @@ python data_loader.py
 --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased
 --test_dataset ptb-xl
 ```
-### 1.3.2 sample of ECG-QA-FSL dataset
 
+## 1.3 sample of ECG-QA-FSL dataset
 
 ```python
 episode = {
@@ -159,29 +100,22 @@ episode = {
 }
 ```
 
-### 1.3.3 Data Splits
-
-- Number of instances in each split (train/test): 8:2
-- Criteria: first split based on template id (no expression overlap between train/test), then random split for support/query set in few-shot tasks.
-
-### 1.3.4 ECG-QA-FSL dataset for meta-learning
-<img src="img/FSL_ECG_QAMeta-Learning.png" alt="Few-shot Meta-learning Example" width="600"/>
+### 1.4 New Datasets Class distribution
 
-# 2. Summary
-
-## 2.1 Curation Rationale
-
-Developing robust and reliable multimodal QA systems for ECG interpretation relies on the availability of both high-quality and large quantities of labeled data. Meta-learning, a paradigm focused on “learning to learn”, enables them to acquire transferable knowledge and adapt rapidly to new, unseen tasks with minimal labeled data.
+| Question Type   | Attributes | Answers                          | Classes (train:test) | Samples | Example                                                                 |
+|-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
+| Single-Verify   | 94         | yes/no                           | 156 (124:32)          | 34,105  | Q: Does this ECG show 1st degree av block? <br> A: yes/no               |
+| Single-Choose   | 165        | both/none/attr_1/attr_2          | 262 (209:53)          | 47,655  | Q: Which noise does this ECG show, baseline drift or static noise? <br> A:  baseline drift /static noise   |
+| 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 | ...                                                                     |
 
-## 2.2 Citation
+# 2 Citation
 
 ```
-@inproceedings{10888594,
+@article{tang2024electrocardiogram,
+  title={Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning},
   author={Tang, Jialu and Xia, Tong and Lu, Yuan and Mascolo, Cecilia and Saeed, Aaqib},
-  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
-  title={Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling}, 
-  year={2025},
-  pages={1-5},
-  doi={10.1109/ICASSP49660.2025.10888594}
+  journal={arXiv preprint arXiv:2410.14464},
+  year={2024}
 }
 ```