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MIST-HER2

Dataset Information

The MIST dataset is a staining transformation dataset related to breast cancer diagnosis, containing aligned H&E-IHC images for four crucial biomarkers in breast cancer diagnosis: HER2, Ki67, ER, and PR. Specifically, the MIST dataset provides over 4000 training samples and 1000 test samples of aligned H&E-IHC images for each biomarker's IHC staining.

Due to the correlation between the morphological information of H&E stained sections and the molecular information of IHC stained sections, the transformation from H&E to IHC staining is feasible. Compared to H&E staining, IHC staining is significantly more costly, including higher labor demands and more expensive laboratory equipment. The MIST dataset supports the development of H&E to IHC image staining transformation technologies, which not only helps reduce experimental costs and manual labor but also enhances the accuracy and efficiency of breast cancer diagnosis.

Dataset Meta Information

Dimensions Modality Task Type Number of Categories Data Volume File Format
2D pathology Image Transformation 4 22688 JPG

Resolution Details

Dataset Statistics size
min (1024, 1024)
median (1024, 1024)
max (1024, 1024)

Label Information Statistics

Biomarker HER2 Ki67 ER PR
Count 5642 5361 5153 5139
Percentage 27.82% 26.44% 25.41% 25.34%

Visualization

H&E image (a) and corresponding IHC image (b), from the original paper.

File Structure

MIST
│
├── PR
├────TrainValAB
├─────trainA
├──────1.jpg
├──────2.jpg
├──────...
├─────trainB
├─────valA
├─────valB
├── Ki67
├── HER2
├── ER

Authors and Institutions

Fangda Li (Purdue University, West Lafayette)

Zhiqiang Hu (Sensetime Research)

Wen Chen (Sensetime Research)

Avinash Kak (Purdue University, West Lafayette)

Source Information

Official Website: https://link.springer.com/chapter/10.1007/978-3-031-43987-2_61

Download Link: https://drive.google.com/drive/folders/146V99Zv1LzoHFYlXvSDhKmflIL-joo6p?usp=sharing

Article Address: https://link.springer.com/chapter/10.1007/978-3-031-43987-2_61

Publication Date: 2023-10

Citation

@inproceedings{li2023adaptive,
  title={Adaptive supervised patchnce loss for learning h\&e-to-ihc stain translation with inconsistent groundtruth image pairs},
  author={Li, Fangda and Hu, Zhiqiang and Chen, Wen and Kak, Avinash},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={632--641},
  year={2023},
  organization={Springer}
}

Original introduction article is here.