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+# 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](https://iebil.di.unimi.it/cnnALL/index.htm)
+    
+Outline:
+![Outline](https://iebil.di.unimi.it/cnnALL/imgs/outline_civemsa21all.jpg "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/ <br/>
+    ADP database, split in patches, obtained following the instructions at: <br/>
+    https://www.dsp.utoronto.ca/projects/ADP/ <br/>
+    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: <br/>
+    https://www.dsp.utoronto.ca/projects/ADP/ <br/>
+    
+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 <br/>
+    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)<br/>
+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) <br/>
+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]
+