Update README.md

README.md

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-# Enhanced MedMNIST Dataset
+# Cross-Dimensional Evaluation Datasets
 
-MedMNIST is a comprehensive collection of standardized biomedical images designed for various analytical tasks in the medical field. This dataset has been expanded to include three new subsets, broadening the range of imaging modalities and classification challenges available to researchers. These additions complement the existing MedMNIST collections, offering a more diverse set of resources for developing and evaluating machine learning models across various medical imaging applications.
+Transfer learning in machine learning models, particularly deep learning architectures, requires diverse datasets to ensure robustness and generalizability across tasks and domains. This repository provides comprehensive details on the datasets used for evaluation, categorized into **2D** and **3D datasets**. These datasets span variations in image dimensions, pixel ranges, label types, and unique labels, facilitating a thorough assessment of fine-tuning capabilities.
 
-## Overview of 
+## 2D Datasets
 
+The 2D datasets span a range of medical imaging modalities and classification tasks. They vary in complexity, from binary classification to multi-class problems, and are standardized to ensure consistent preprocessing. All images have dimensions of `(224, 224)` and pixel values normalized to the range `[0, 255]`.
 
+### Overview of 2D Datasets
 
-These datasets are integrated into the MedMNIST collection, which includes both 2D and 3D biomedical images across various modalities and tasks.
+| **Dataset**        | **Modality**    | **Samples** | **Image Dimensions** | **Pixel Range** | **Unique Labels** | **Label Type** |
+|---------------------|-----------------|-------------|-----------------------|------------------|--------------------|----------------|
+| Blood \[1\]        | Microscope      | 17,092      | (224, 224, 3)        | 0 -- 255        | 8                  | Multi-class    |
+| Brain \[2\]         | MRI             | 1,600       | (224, 224, 3)        | 0 -- 255        | 23                 | Multi-class    |
+| Brain Tumor \[3\]   | MRI             | 3,064       | (224, 224, 3)        | 0 -- 255        | 3                  | Multi-class    |
+| Breast Cancer \[4\] | US              | 1,875       | (224, 224, 3)        | 0 -- 255        | 2                  | Binary         |
+| Breast \[5\]        | US              | 780         | (224, 224, 1)        | 0 -- 255        | 2                  | Binary         |
+| Derma \[6\]         | Dermatology     | 10,015      | (224, 224, 3)        | 0 -- 255        | 7                  | Multi-class    |
+| OrganC \[7\]        | CT              | 23,582      | (224, 224, 1)        | 0 -- 255        | 11                 | Multi-class    |
+| OrganS \[8\]        | CT              | 25,211      | (224, 224, 1)        | 0 -- 255        | 11                 | Multi-class    |
+| Pneumonia \[9\]     | XR              | 5,856       | (224, 224, 1)        | 0 -- 255        | 2                  | Binary         |
 
-## MedMNIST2D Datasets and Statistics
+### Insights into 2D Datasets
 
-MedMNIST2D comprises twelve pre-processed 2D datasets from selected sources, covering primary data modalities such as X-Ray, OCT, Ultrasound, and more. Each dataset is tailored for specific classification tasks, including binary and multi-class classifications, with varying numbers of samples.
+- **Blood**: 17,092 microscope images across 8 classes. Excellent for testing models on complex multi-class classification.
+- **Brain**: 1,600 MRI images with 23 labels, providing a challenging multi-class scenario.
+- **Brain Tumor**: 3,064 MRI images in 3 classes, focused on tumor detection and classification.
+- **Breast Cancer**: 1,875 ultrasound images (binary labels), suitable for cancer detection benchmarks.
+- **Breast**: 780 ultrasound images with binary labels, ideal for evaluating performance in small datasets.
+- **Derma**: 10,015 dermatology images across 7 classes, critical for skin lesion classification.
+- **OrganC & OrganS**: 23,582 and 25,211 CT images respectively, focused on organ classification and segmentation tasks.
+- **Pneumonia**: 5,856 X-ray images for binary classification of lung infections.
 
-| **Dataset Name**   | **Data Modality**       | **Task (Number of Classes)** | **Number of Samples** |
-|--------------------|-------------------------|------------------------------|-----------------------|
-|_2D Datasets_ | | |
-| BreastMNIST        | Ultrasound       | Binary-Class (2)             | 780                   |
-| OrganAMNIST        | Abdominal CT            | Multi-Class (11)             | 58,830                |
-| OrganCMNIST        | Abdominal CT            | Multi-Class (11)             | 23,583                |
-| OrganSMNIST        | Abdominal CT            | Multi-Class (11)             | 25,211                |
-|_Additional 2D Datasets_ | | |
-| Brain Tumor Dataset  | Magnetic Resonance      | Multi-Class (3)              | 3,064                  |
-| Brain Dataset        | Magnetic Resonance      | Multi-Class (23)             | 1,600                  |
-| Breast Cancer        | Ultrasound              | Binary-Class (2)             | 1,875                  |
-|_Out-of-Distribution_ | | |
-| BloodMNIST         | Blood Cell Microscope   | Multi-Class (8)              | 17,092                |
-| PneumoniaMNIST     | Chest X-Ray             | Binary-Class (2)             | 5,856                 |
-| DermaMNIST         | Dermatoscope            | Multi-Class (7)              | 10,015                |
+---
 
-These datasets are pre-processed into a standardized format, facilitating ease of use for machine learning applications.
+## 3D Datasets
 
-## MedMNIST3D Datasets and Statistics
+3D datasets provide volumetric data essential for tasks like segmentation and spatial analysis in medical imaging. These datasets test models' capabilities in handling 3D spatial information.
 
-MedMNIST3D comprises six pre-processed 3D datasets from selected sources, covering primary data modalities such as CT and MRI scans. Each dataset is tailored for specific classification tasks, including binary and multi-class classifications, with varying numbers of samples.
+### Overview of 3D Datasets
 
-| **Dataset Name**   | **Data Modality**       | **Task (Number of Classes)** | **Number of Samples** |
-|--------------------|-------------------------|------------------------------|-----------------------|
-| OrganMNIST3D       | Abdominal CT            | Multi-Class (11)             | 1,742                 |
-| NoduleMNIST3D      | Chest CT                | Binary-Class (2)             | 1,633                 |
-| AdrenalMNIST3D     | Abdominal CT            | Binary-Class (2)             | 1,584                 |
-| FractureMNIST3D    | Chest CT                | Multi-Class (3)              | 1,370                 |
-|_Out-of-Distribution_ | | |
-| VesselMNIST3D      | Brain MRA               | Binary-Class (2)             | 1,908                 |
-| SynapseMNIST3D     | Electron Microscope     | Binary-Class (2)             | 1,759                 |
+| **Dataset**              | **Modality**    | **Samples** | **Image Dimensions**    | **Pixel Range**     | **Unique Labels** | **Label Type** |
+|--------------------------|-----------------|-------------|--------------------------|----------------------|--------------------|----------------|
+| BraTS21 \[10\]          | MRI             | 585         | (3, 96, 96, 96)         | 0 -- 22,016         | 2                  | Binary         |
+| BUSV \[11\]             | US              | 186         | (1, 96, 96, 96)         | 0 -- 255            | 2                  | Binary         |
+| Fracture \[12\]         | CT              | 1,370       | (1, 64, 64, 64)         | 0 -- 255            | 3                  | Multi-class    |
+| Lung Adenocarcinoma \[13\] | CT           | 1,050       | (1, 128, 128, 128)      | -1,450 -- 3,879     | 3                  | Multi-class    |
+| Mosmed \[14\]           | CT              | 200         | (1, 128, 128, 64)       | 0 -- 1              | 2                  | Binary         |
+| Synapse \[15\]          | Microscope      | 1,759       | (1, 64, 64, 64)         | 0 -- 255            | 2                  | Binary         |
+| Vessel \[16\]           | MRA             | 1,908       | (1, 64, 64, 64)         | 0 -- 255            | 2                  | Binary         |
+| IXI (Gender) \[17\]     | MRI             | 561         | (2, 160, 192, 224)      | 0 -- 255            | 2                  | Binary         |
 
-These datasets are pre-processed into a standardized format, facilitating ease of use for machine learning applications.
+### Insights into 3D Datasets
 
-## Accessing the Dataset
+- **BraTS21**: 585 MRI scans for binary brain tumor classification, testing volumetric segmentation and analysis.
+- **BUSV**: 186 ultrasound volumes with binary labels, focusing on breast ultrasound imaging.
+- **Fracture**: 1,370 CT volumes in 3 classes, assessing bone fracture detection.
+- **Lung Adenocarcinoma**: 1,050 CT volumes for classifying lung adenocarcinoma subtypes.
+- **Mosmed**: 200 CT volumes for detecting COVID-19-related lung infections.
+- **Synapse**: 1,759 microscope volumes for neural imaging classification and segmentation.
+- **Vessel**: 1,908 MRA volumes for vessel segmentation.
+- **IXI (Gender)**: 561 MRI volumes labeled by gender, testing demographic classification from brain imaging.
 
-The enhanced MedMNIST collection, including both existing and new datasets, is accessible on Hugging Face. Researchers and practitioners can utilize this resource to develop and evaluate machine learning models across various medical imaging applications.
+---
 
-## Citation
+## Dataset Diversity and Evaluation Suitability
 
-If you find this dataset useful, please cite the following papers:
+These datasets collectively provide:
 
-```
-@inproceedings{yavuz2025policy,
-  title={Policy Gradient-Driven Noise Mask},
-  author={Yavuz, Mehmet Can and Yang, Yang},
-  booktitle={International Conference on Pattern Recognition},
-  pages={414--431},
-  year={2025},
-  organization={Springer}
-}
+- **Diverse Modalities**: Covering microscopy, CT, MRI, ultrasound, X-ray, and more.
+- **Wide Complexity Range**: From binary classification to multi-class problems.
+- **Standardized Preprocessing**: Uniform image dimensions and pixel scaling.
+- **Scenarios with Varying Data Size**: From small datasets (e.g., BUSV) to large-scale datasets (e.g., OrganS).
+- **Volumetric Data for 3D Analysis**: Testing models' spatial reasoning capabilities.
 
-@article{medmnistv2,
-  title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
-  author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
-  journal={Scientific Data},
-  volume={10},
-  number={1},
-  pages={41},
-  year={2023},
-  publisher={Nature Publishing Group UK London}
-}
+These datasets are curated to facilitate robust and generalizable machine learning models for real-world medical applications.
 
-@inproceedings{medmnistv1,
-  title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
-  author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
-  booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
-  pages={191--195},
-  year={2021}
-}
-```
-
-Please also cite the corresponding papers of the source data if you use any subset of MedMNIST, as per the description on the project website.
-
-## License
-
-This project is licensed under the MIT License. Each subset retains the same license as that of the source dataset. Please refer to the project website for more details.
-
-*Note: This dataset is NOT intended for clinical use.* 
+---
+license: mit
+---