Update info.txt

info.txt

@@ -1,87 +1,120 @@
-# MedCAGD-Dataset-Collection - Medical Image Segmentation Datasets
-
-This repo contains multiple publicly available medical image segmentation (Semantic Segmentation) datasets used for training and evaluation of the segmentation models. Each subfolder corresponds to a specific dataset and follows its original structure or a standardized format used in this project. These datasets are used for benchmarking segmentation performance across multiple medical imaging modalities including CT, MRI, dermoscopy, endoscopy, ultrasound, fundus imaging, and microscopy.
-
-## Citation
-
-If you use this dataset collection or the benchmark results in your research, please cite the following paper:
-
-```bibtex
-@inproceedings{wazir2025rethinking,
-  title={Rethinking decoder design: Improving biomarker segmentation using depth-to-space restoration and residual linear attention},
-  author={Wazir, Saad and Kim, Daeyoung},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={30861--30871},
-  year={2025},
-  doi = {10.48550/arXiv.2506.18335},
-  url = {https://doi.org/10.48550/arXiv.2506.18335}
-}
-```
-
-## Research Note
-
-This dataset collection is used to benchmark segmentation models across multiple medical imaging datasets. The segmentation benchmarks provided here are part of ongoing research related to the MCADS decoder and the upcoming MedCAGD framework. The full benchmark results and evaluation protocols will appear in the MedCAGD paper, which is currently under review, and additional results will be released after the review process.
-
-_____________________________________________________________________________
-
-## Included Datasets:
-
-ACDC-2D-Slices
-2D cardiac MRI slices from the ACDC dataset used for cardiac structure segmentation.
-
-Synapse Multi-Organ Segmentation Dataset-8 abdominal organs-2D-Slices  
-2D slices from the Synapse multi-organ CT segmentation dataset containing annotations for eight abdominal organs.
-
-ThyroidXL  
-Thyroid ultrasound dataset for thyroid nodule segmentation.
-
-ISIC2017  
-Skin lesion segmentation dataset from the ISIC 2017 challenge.
-
-ISIC2018  
-Skin lesion segmentation dataset from the ISIC 2018 challenge.
-
-BKAI  
-Gastrointestinal polyp segmentation dataset released by BKAI.
-
-ClinicDB  
-Colonoscopy polyp segmentation dataset from the CVC-ClinicDB benchmark.
-
-ColonDB  
-Colonoscopy polyp segmentation dataset commonly used for evaluating polyp detection models.
-
-ETIS  
-ETIS-Larib polyp dataset containing challenging colonoscopy images with pixel-level annotations.
-
-Kvasir  
-Kvasir-SEG dataset for gastrointestinal polyp segmentation.
-
-BUSI  
-Breast ultrasound dataset used for tumor segmentation.
-
-DRIVE  
-Retinal vessel segmentation dataset from the DRIVE challenge.
-
-FIVES  
-Fundus Image Vessel Segmentation dataset for retinal blood vessel analysis.
-
-CHASEDB
-Retinal vessel segmentation dataset from the CHASE_DB1 benchmark.
-
-LES-AV  
-Dataset for retinal artery and vein segmentation.
-
-STARE  
-Retinal vessel segmentation dataset from the STARE benchmark.
-
-UOA-DR  
-Diabetic retinopathy dataset with retinal lesion annotations.
-
-CellSeg  
-Microscopy cell segmentation dataset used for cell instance or semantic segmentation.
-
-____________________________________________________________________________
-
-
-## Acknowledgement
-We gratefully acknowledge the prior contributions of the research community, which have provided the foundation for our framework.
+# MedCAGD-Dataset-Collection - Medical Image Segmentation Datasets
+
+This repo contains multiple publicly available medical image segmentation (Semantic Segmentation) datasets used for training and evaluation of the segmentation models. Each subfolder corresponds to a specific dataset and follows its original structure or a standardized format used in this project. These datasets are used for benchmarking segmentation performance across multiple medical imaging modalities including CT, MRI, dermoscopy, endoscopy, ultrasound, fundus imaging, and microscopy.
+
+## Citation
+
+If you use this dataset collection or the benchmark results in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{wazir2025rethinking,
+  title={Rethinking decoder design: Improving biomarker segmentation using depth-to-space restoration and residual linear attention},
+  author={Wazir, Saad and Kim, Daeyoung},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={30861--30871},
+  year={2025},
+  doi = {10.48550/arXiv.2506.18335},
+  url = {https://doi.org/10.48550/arXiv.2506.18335}
+}
+```
+
+## Research Note
+
+This dataset collection provides early access to the datasets used for benchmarking segmentation models across multiple medical imaging datasets. The benchmark results for the methods evaluated on these datasets are currently under review and have not yet been published.
+The segmentation benchmarks associated with this dataset collection are part of ongoing research related to the MCADS decoder and the upcoming MedCAGD framework. The full benchmark results and evaluation protocols will appear in the MedCAGD paper, which is currently under review, and additional results will be released after the review process.
+_____________________________________________________________________________
+
+## Included Datasets:
+
+ACDC-2D-Slices
+2D cardiac MRI slices from the ACDC dataset used for cardiac structure segmentation.
+
+Synapse Multi-Organ Segmentation Dataset-8 abdominal organs-2D-Slices  
+2D slices from the Synapse multi-organ CT segmentation dataset containing annotations for eight abdominal organs.
+
+ThyroidXL  
+Thyroid ultrasound dataset for thyroid nodule segmentation.
+
+ISIC2017  
+Skin lesion segmentation dataset from the ISIC 2017 challenge.
+
+ISIC2018  
+Skin lesion segmentation dataset from the ISIC 2018 challenge.
+
+BKAI  
+Gastrointestinal polyp segmentation dataset released by BKAI.
+
+ClinicDB  
+Colonoscopy polyp segmentation dataset from the CVC-ClinicDB benchmark.
+
+ColonDB  
+Colonoscopy polyp segmentation dataset commonly used for evaluating polyp detection models.
+
+ETIS  
+ETIS-Larib polyp dataset containing challenging colonoscopy images with pixel-level annotations.
+
+Kvasir  
+Kvasir-SEG dataset for gastrointestinal polyp segmentation.
+
+BUSI  
+Breast ultrasound dataset used for tumor segmentation.
+
+DRIVE  
+Retinal vessel segmentation dataset from the DRIVE challenge.
+
+FIVES  
+Fundus Image Vessel Segmentation dataset for retinal blood vessel analysis.
+
+CHASEDB
+Retinal vessel segmentation dataset from the CHASE_DB1 benchmark.
+
+LES-AV  
+Dataset for retinal artery and vein segmentation.
+
+STARE  
+Retinal vessel segmentation dataset from the STARE benchmark.
+
+UOA-DR  
+Diabetic retinopathy dataset with retinal lesion annotations.
+
+CellSeg  
+Microscopy cell segmentation dataset used for cell instance or semantic segmentation.
+
+____________________________________________________________________________
+
+
+## Acknowledgement
+We gratefully acknowledge the prior contributions of the research community, which have provided the foundation for our framework.
+
+
+---
+license: mit
+language:
+- en
+tags:
+- MCADS-Decoder
+- medical-image-segmentation
+- semantic-segmentation
+- computer-vision
+- multi-class-segmentation
+- Synapse
+- ThyroidXL
+- ISIC2017
+- ISIC2018
+- BKAI
+- ClinicDB
+- ColonDB
+- ETIS
+- Kvasir
+- BUSI
+- DRIVE
+- FIVES
+- CHASEDB
+- LES-AV
+- STARE
+- UOA-DR
+- CellSeg
+
+task_categories:
+- image-segmentation
+---