Awesome
ScoreNet
PyTorch implementation of "ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification".
Datasets
BRACS
The BRACS dataset is organized as follows:
tree -d BRACS/BRACS_RoI/previous
BRACS/BRACS_RoI/previous
├── test
│ ├── 0_N
│ ├── 1_PB
│ ├── 2_UDH
│ ├── 3_ADH
│ ├── 4_FEA
│ ├── 5_DCIS
│ └── 6_IC
├── train
│ ├── 0_N
│ ├── 1_PB
│ ├── 2_UDH
│ ├── 3_ADH
│ ├── 4_FEA
│ ├── 5_DCIS
│ └── 6_IC
└── val
├── 0_N
├── 1_PB
├── 2_UDH
├── 3_ADH
├── 4_FEA
├── 5_DCIS
└── 6_IC
Note that to be able to compare with existing baselines we used the "previous" version of the dataset.
BACH
The BACH dataset is organized as follows:
tree -d ICIAR2018_BACH_Challenge/Photos/
ICIAR2018_BACH_Challenge/Photos/
├── Benign
├── InSitu
├── Invasive
└── Normal
Acknowledgment
This code relies on some elements of DINO and the accompanying code of Differentiable Patch Selection for Image Recognition.
Cite
@inproceedings{stegmuller2023scorenet,
title={Scorenet: Learning non-uniform attention and augmentation for transformer-based histopathological image classification},
author={Stegm{\"u}ller, Thomas and Bozorgtabar, Behzad and Spahr, Antoine and Thiran, Jean-Philippe},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={6170--6179},
year={2023}
}