HistoTNet

Pytorch source code for the paper:

A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti, 
"Histopathological transfer learning for Acute Lymphoblastic Leukemia detection", 
in Proc. of the 2021 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2021), 
June 18-20, 2021, pp. 1-6. 
ISBN: 978-1-6654-1249-0. [DOI: 10.1109/CIVEMSA52099.2021.9493677]

Paper:

https://ieeexplore.ieee.org/document/9493677

Project page:

https://iebil.di.unimi.it/cnnALL/index.htm

Outline:

Citation:

@InProceedings {civemsa21all,
author = {A. Genovese and M. S. Hosseini and V. Piuri and K. N. Plataniotis and F. Scotti},
booktitle = {Proc. of the 2021 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2021)},
title = {Histopathological transfer learning for Acute Lymphoblastic Leukemia detection},
month = {June},
day = {18-20},
year = {2021},}

Main files:

• (1) PyTorch_HistoNet/pytorch_histonet.py: training/testing of the HistoNet;
• (2) PyTorch_HistoTNet/pytorch_histotnet.py: training/testing of the HistoTNet.

Instructions:

0. Install the required packages (see packages.txt)

1. cd to "(1) PyTorch_HistoNet" and run "pytorch_histonet.py" to train the HistoNet on the ADP database, implemented according to the paper:

   Mahdi S. Hosseini, Lyndon Chan, Gabriel Tse, Michael Tang, Jun Deng, Sajad Norouzi, Corwyn Rowsell, Konstantinos N. Plataniotis, Savvas Damaskinos "Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11747-11756

   Required files:

   • (1) PyTorch_HistoNet/db_orig/ADP/img_res_1um_bicubic/
     ADP database, split in patches, obtained following the instructions at:
     https://www.dsp.utoronto.ca/projects/ADP/
     e.g., (1) PyTorch_HistoNet/db_orig/ADP/img_res_1um_bicubic/001.png_crop_16.png

   • (1) PyTorch_HistoNet/db_orig/ADP/ADP_EncodedLabels_Release1_Flat.csv file containing the labels of the ADP database, obtained following the instructions at:
     https://www.dsp.utoronto.ca/projects/ADP/
     

2. Copy the trained models in "(2) PyTorch_HistoTNet\pretrained_nets". For simplicity, some trained models are already present.

3. cd to "(2) PyTorch_HistoTNet" and run "pytorch_histotnet.py" to train the HistoTNet on the ALL-IDB database for Acute Lymphoblastic Leukemia detection.

   Required files:

   • (2) PyTorch_HistoTNet/db/ALL_IDB2
     ALL-IDB database, obtained following the instructions at: https://homes.di.unimi.it/scotti/all/ e.g., (2) PyTorch_HistoTNet/db/ALL_IDB2/Im001_1.tif

The databases used in the paper can be obtained at:

• Atlas of Digital Pathology (ADP)
  https://www.dsp.utoronto.ca/projects/ADP/

  Mahdi S. Hosseini, Lyndon Chan, Gabriel Tse, Michael Tang, Jun Deng, Sajad Norouzi, Corwyn Rowsell, Konstantinos N. Plataniotis, Savvas Damaskinos "Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11747-11756

• Acute Lymphoblastic Leukemia Image Database for Image Processing (ALL-IDB)
  https://homes.di.unimi.it/scotti/all/

  R. Donida Labati, V. Piuri, F. Scotti "ALL-IDB: the acute lymphoblastic leukemia image database for image processing" in Proc. of the 2011 IEEE Int. Conf. on Image Processing (ICIP 2011), Brussels, Belgium, pp. 2045-2048, September 11-14, 2011. ISBN: 978-1-4577-1302-6. [DOI: 10.1109/ICIP.2011.6115881]