From 313e3d3b65121404795d53b92cbfbca606014d8c Mon Sep 17 00:00:00 2001 From: Azad Aminpour Date: Wed, 14 Mar 2018 10:14:18 -0400 Subject: [PATCH 01/15] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 942408c..6be863b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # ICIAR2018 -This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. +This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. The paper associated with this implementation is available at https://arxiv.org/abs/1803.04054 . We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives patches of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the whole slide (P) times the depth of the feature maps generted by the first network (C):

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za$^#WxFHSvy92WG%Y^csL(E$Z{xM_sMG_2C4-J+xIANL6vgg$0)%2YwANtf`R;q}S zU^b(Q+{fJ@TcL{dYMwybv;FR<)l%F zQ#)6q&hN<|e$0OjY%{W50`78e0ybn!lmA>-PIE&u0uiG7OvUu(VBrhv3MSxE$qH#? zG;RHZ6DdAH|Uf&@}PH%JCbgg5oL8M~ZA1=qE1mv*EbJkZp3Z|ZvWcCmp zAG$#|?<-xWmFe9>x%nnMh%e8VWlV#s?fxr)F^?EWVf07W)#7Sn!p@O`p$F&zYo%q+ z$Nz5^zx_4m7QXiE@U_02Q&m^LIhSo%5SZIm>kCY^Ya0@^L0jV0+(0|Cz{8rTBxukG z+g@?-+&rjt0R&(YSi=FCm?U_l5IRpH{op^h0fS!91B(Ww*gTL{Pgg&ebxsLQ03dGY AGynhq From c660889ef7214202ca3c05b2684ae1d73ba203da Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Fri, 30 Mar 2018 18:07:57 -0400 Subject: [PATCH 03/15] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6be863b..f3d996b 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ICIAR2018 This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. The paper associated with this implementation is available at https://arxiv.org/abs/1803.04054 . -We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives patches of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the whole slide (P) times the depth of the feature maps generted by the first network (C): +We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patch) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the whole slide in a non-overlapping fashion (12 path) times the depth of the feature maps generted by the first network (C):

From 3d9c4eba60cf1332346ff0da9a1fd2fd8c068a58 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Fri, 30 Mar 2018 18:08:35 -0400 Subject: [PATCH 04/15] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f3d996b..ee80c5e 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ICIAR2018 This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. The paper associated with this implementation is available at https://arxiv.org/abs/1803.04054 . -We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patch) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the whole slide in a non-overlapping fashion (12 path) times the depth of the feature maps generted by the first network (C): +We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patches) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the whole slide in a non-overlapping fashion (12 patches) times the depth of the feature maps generted by the first network (C):

From 74212b6cbd020c960b045596768d77bb8ce1e5a3 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Sat, 19 May 2018 10:35:52 -0400 Subject: [PATCH 05/15] Update README.md --- README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/README.md b/README.md index ee80c5e..6e95517 100644 --- a/README.md +++ b/README.md @@ -61,3 +61,15 @@ python validate.py ```bash python train.py --channels 1 ``` + +## Citation +If you use this code for your research, please cite our paper Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification: + +``` +@article{nazeri2018two, + title={Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification}, + author={Nazeri, Kamyar and Aminpour, Azad and Ebrahimi, Mehran}, + journal={arXiv preprint arXiv:1803.04054}, + year={2018} +} +``` From 2109d6d231b94dc4ea221c4d1b83b21b78bb36ad Mon Sep 17 00:00:00 2001 From: KamyarNazeri Date: Sat, 19 May 2018 10:41:06 -0400 Subject: [PATCH 06/15] add 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Subject: [PATCH 07/15] Update README.md --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 6e95517..d29988d 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ICIAR2018 -This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. The paper associated with this implementation is available at https://arxiv.org/abs/1803.04054 . +This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer Histology (BACH) images for automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma and invasive carcinoma. -We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patches) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the whole slide in a non-overlapping fashion (12 patches) times the depth of the feature maps generted by the first network (C): +We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patches) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the microscopy image in a non-overlapping fashion (12 patches) times the depth of the feature maps generted by the first network (C):

@@ -25,7 +25,10 @@ cd ICIAR2018 ``` ### Dataset -- We use the ICIAR2018 dataset. To train a model on the full dataset, please download it from the [official website](https://iciar2018-challenge.grand-challenge.org/dataset/) (registration required). +- We use the ICIAR2018 dataset. To train a model on the full dataset, please download it from the [official website](https://iciar2018-challenge.grand-challenge.org/dataset/) (registration required). The dataset is composed of 400 high resolution Hematoxylin and Eosin (H&E) stained breast histology microscopy images labelled as normal, benign, in situ carcinoma, and invasive carcinoma (100 images for each category): +

+ +

After downloading, please put it under the `datasets` folder in the same way the sub-directories are provided. 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Subject: [PATCH 09/15] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d29988d..7d72f28 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ cd ICIAR2018 ### Dataset - We use the ICIAR2018 dataset. To train a model on the full dataset, please download it from the [official website](https://iciar2018-challenge.grand-challenge.org/dataset/) (registration required). The dataset is composed of 400 high resolution Hematoxylin and Eosin (H&E) stained breast histology microscopy images labelled as normal, benign, in situ carcinoma, and invasive carcinoma (100 images for each category):

- +

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zckEn1L(DZ?jFexuuVr~n$nM$2o{Q!r{yv%lxe-R_%_lnt6j6BcDJGxDfF;pSZg$={wiA6&$v#Y5}6_9N3(wc zY&P@+;6^Z;SZ!28vXHDIz}j~mVeQIy`JB2@?)~AH>K_glutQdac17>|?IG#9&*-OH zPLl}!L)r$E^_CDwRx_317;-}6DIr7o?4%%V68WwAuof|NSo!_(y4)4L?@v5Zf2n4=G^#yT#T$|&NWtE8% z>nW@HDUE6Q_(2J2Vm>nZf!h8fKHRz#|65B*g8C5=vW2KXzpoG14=O-6iHkRsS*b`=2Lk|GUm|oJ{^3 DKq?=D From e0c4a16a4bc40c8c7f186bac95e571b830753249 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Sat, 19 May 2018 10:53:56 -0400 Subject: [PATCH 11/15] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7d72f28..b5070be 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ This repository is the part A of the ICIAR 2018 Grand Challenge on BreAst Cancer We are presenting a CNN approach using two convolutional networks to classify histology images in a patchwise fashion. The first network, receives overlapping patches (35 patches) of the whole-slide image and learns to generate spatially smaller outputs. The second network is trained on the downsampled patches of the whole image using the output of the first network. The number of channels in the input to the second network is equal to the total number of patches extracted from the microscopy image in a non-overlapping fashion (12 patches) times the depth of the feature maps generted by the first network (C):

- +

## Prerequisites From c0a01bf2af96f2925e602ccd731ea818d4da8180 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Sun, 20 May 2018 17:07:50 -0400 Subject: [PATCH 12/15] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b5070be..107653a 100644 --- a/README.md +++ b/README.md @@ -14,9 +14,9 @@ We are presenting a CNN approach using two convolutional networks to classify hi ## Getting Started ### Installation - Install PyTorch and dependencies from http://pytorch.org -- Install python scikit-learn libraries [scikit-learn](https://github.com/scikit-learn/scikit-learn). +- Install python requirements: ```bash -pip install -U scikit-learn +pip install -r requirements.txt ``` - Clone this repo: ```bash From b117267b8c3c252325fdb22da7d6bd769da32679 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Sun, 20 May 2018 17:08:32 -0400 Subject: [PATCH 13/15] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 107653a..7517aea 100644 --- a/README.md +++ b/README.md @@ -13,16 +13,16 @@ We are presenting a CNN approach using two convolutional networks to classify hi ## Getting Started ### Installation -- Install PyTorch and dependencies from http://pytorch.org -- Install python requirements: -```bash -pip install -r requirements.txt -``` - Clone this repo: ```bash git clone https://github.com/ImagingLab/ICIAR2018 cd ICIAR2018 ``` +- Install PyTorch and dependencies from http://pytorch.org +- Install python requirements: +```bash +pip install -r requirements.txt +``` ### Dataset - We use the ICIAR2018 dataset. To train a model on the full dataset, please download it from the [official website](https://iciar2018-challenge.grand-challenge.org/dataset/) (registration required). The dataset is composed of 400 high resolution Hematoxylin and Eosin (H&E) stained breast histology microscopy images labelled as normal, benign, in situ carcinoma, and invasive carcinoma (100 images for each category): From c3e3d8dd4a2993cbe3a64b9ebd299902660a6446 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Sun, 20 May 2018 17:09:57 -0400 Subject: [PATCH 14/15] Update requirements.txt --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index bfdddea..d09485f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ numpy==1.14.3 scipy==1.0.1 future==0.16.0 -matplotlib=2.2.2 +matplotlib==2.2.2 pillow==5.0.0 -scikit-learn==0.19.1 \ No newline at end of file +scikit-learn==0.19.1 From fc2908d9ba24de9d10e69e47854aa207f1ed0da9 Mon Sep 17 00:00:00 2001 From: Kamyar Nazeri Date: Sun, 20 May 2018 17:12:24 -0400 Subject: [PATCH 15/15] Update requirements.txt --- requirements.txt | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/requirements.txt b/requirements.txt index d09485f..bafdf95 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ -numpy==1.14.3 -scipy==1.0.1 -future==0.16.0 -matplotlib==2.2.2 -pillow==5.0.0 -scikit-learn==0.19.1 +numpy ~=1.14.3 +scipy ~= 1.0.1 +future ~= 0.16.0 +matplotlib ~= 2.2.2 +pillow != 5.0.0 +scikit-learn ~= 0.19.1

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