/histopathology_analysis

Deep learning methods for histopathology image analysis

BSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Deep learning methods for histopathology image analysis

Codes for deep learning-based pipelines for whole slide tissue image (WSI) analysis:

Segmentation of Nuclei:

Code Repository: 
   https://bit.ly/2IEMDp8 (https://github.com/SBU-BMI/quip_cnn_segmentation)
   Trained CNN model can be downloaded from
   https://bit.ly/3pBHQFE (http://vision.cs.stonybrook.edu/~lehhou/download/model_trained.tar.gz)
          
Related Papers:
   Hou, L., Gupta, R., et al. Dataset of segmented nuclei in hematoxylin 
   and eosin stained histopathology images of ten cancer types. Sci Data7, 185 
   (2020). https://doi.org/10.1038/s41597-020-0528-1
          
   Hou L, Agarwal A, et al. Robust histopathology image analysis: to label or 
   to synthesize?. Proceedings of the IEEE Conference on Computer Vision and 
   Pattern Recognition 2019 (pp. 8533-8542).
          
Datasets:
   https://tinyurl.com/yyqoq2n2

Creating Tumor Infiltrating Lymphocyte (TIL) Maps:

Code Repository:
   Recent codes and models developed using the VGG16 and Inception-V4 networks.
   These are the recommended models for use in TIL analysis. 
       https://bit.ly/38JSqEN (https://github.com/SBU-BMI/quip_classification) 
       Trained VGG16 and Inception-V4 models can be downloaded from
       https://bit.ly/3lArJWA (https://stonybrookmedicine.box.com/shared/static/bl15zu4lwb9cc7ltul15aa8kyrn7kh2d.zip)
      
   Codes used in the Cell Reports paper.
       https://bit.ly/3nnFXuq (https://github.com/SBU-BMI/u24_lymphocyte)  
          
Related Papers:
   Saltz J, Gupta R,  et al. Spatial organization and molecular 
   correlation of tumor-infiltrating lymphocytes using deep learning 
   on pathology images. Cell reports. 2018 Apr 3;23(1):181-93.
          
   Abousamra S, Hou L,  et al. Learning from thresholds: fully 
   automated classification of tumor infiltrating lymphocytes for 
   multiple cancer types. arXiv preprint arXiv:1907.03960. 2019 Jul 9.
          
Datasets:
   Dataset in the Cell Reports paper: 
        https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1
   TIL analysis results from the VGG16 and Inception-V4 models:
   (The data is being uploaded, check back in a few days.)
        https://bit.ly/3kJJDVT (https://stonybrookmedicine.box.com/s/qb4gi6o1dihvds0tuieaclpdbcl03qzu)

TIL/Tumor Quantification in TCGA Breast Cancer WSIs:

Code Repository:
   https://bit.ly/2K8pmwh (https://github.com/SBU-BMI/quip_cancer_segmentation)
   Trained CNN model can be downloaded from
   https://bit.ly/3pzvqyo (https://stonybrookmedicine.box.com/shared/static/1hdfb06lgd08xfbpoly9tjp6c6i665nz.zip)
          
Related Papers:
   Le, Han, Rajarsi Gupta, Le Hou, et al. "Utilizing automated breast 
   cancer detection to identify spatial distributions of tumor infiltrating 
   lymphocytes in invasive breast cancer." The American Journal of Pathology (2020).
          
Datasets: 
   https://bit.ly/2UtZcpO (https://app.box.com/s/1qux9ub21zcvpwao1cf81ar4milxl25x)

Segmentation of Tumor Regions in Pancreatic Cancer Cases

Code Repository:
   https://bit.ly/2IJRLZp (https://github.com/SBU-BMI/quip_paad_cancer_detection.git)
   Trained CNN model can be downloaded from
   https://bit.ly/32Pcn9q (https://github.com/SBU-BMI/quip_prad_cancer_detection/tree/master/data/models_cnn)

Related Papers: 
   Le H, Samaras D, Kurc T, Gupta R, Shroyer K, Saltz J. Pancreatic cancer detection 
   in whole slide images using noisy label annotations. InInternational Conference on 
   Medical Image Computing and Computer-Assisted Intervention 2019 Oct 13 (pp. 541-549). Springer, Cham.