Deep Multi-Magnification Network for multi-class tissue segmentation of whole slide images

Related tags

Deep LearningDMMN
Overview

Deep Multi-Magnification Network

This repository provides training and inference codes for Deep Multi-Magnification Network published here. Deep Multi-Magnification Network automatically segments multiple tissue subtypes by a set of patches from multiple magnifications in histopathology whole slide images.

Prerequisites

  • Python 3.6.7
  • Pytorch 1.3.1
  • OpenSlide 1.1.1
  • Albumentations

Training

The main training code is training.py. The trained segmentation model will be saved under runs/ by default.

In addition to config, you may need to update the following variables before running training.py:

  • n_classes: the number of tissue subtype classes + 1
  • train_file and val_file: the list of training and validation patches
    • Slide patches must be stored as /path/slide_tiles/patch_1.jpg, /path/slide_tiles/patch_2.jpg, ... /path/slide_tiles/patch_N.jpg
    • The coresponding label patches must be stored as /path/label_tiles/patch_1.png, /path/label_tiles/patch_2.png, ... /path/label_tiles/patch_N.png
    • train_file and val_file must be formatted as
     /path/,patch_1
     /path/,patch_2
     ...
     /path/,patch_N
    
  • d: the number of pixels of each class in the training set for weighted cross entropy loss function

Note that pixels labeled as class 0 are unannotated and will not contribute to the training.

Inference

The main inference codes are slidereader_coords.py and inference.py. You first need to run slidereader_coords.py to generate patch coordinates to be segmented in input whole slide images. After generating patch coordinates, you may run inference.py to generate segmentation predictions of input whole slide images. The segmentation predictions will be saved under imgs/ by default.

You may need to update the following variables before running slidereader_coords.py:

  • slides_to_read: the list of whole slide images
  • coord_file: an output file listing all patch coordinates

In adition to model_path and out_path, you may need to update the following variables before running inference.py:

  • n_classes: the number of tissue subtype classes + 1
  • test file: the list of patch coordinates generated by slidereader_coords.py
  • data_path: the path where whole slide images are located

Please download the pretrained breast model here.

Note that segmentation predictions will be generated in 4-bit BMP format. The size limit for 4-bit BMP files is 232 pixels.

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details. (c) MSK

Acknowledgments

Reference

If you find our work useful, please cite our paper:

@article{ho2021,
  title={Deep Multi-Magnification Networks for multi-class breast cancer image segmentation},
  author={Ho, David Joon and Yarlagadda, Dig V.K. and D'Alfonso, Timothy M. and Hanna, Matthew G. and Grabenstetter, Anne and Ntiamoah, Peter and Brogi, Edi and Tan, Lee K. and Fuchs, Thomas J.},
  journal={Computerized Medical Imaging and Graphics},
  year={2021},
  volume={88},
  pages={101866}
}
Owner
Computational Pathology
Computational Pathology
Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch

PyTorch implementation of Continuous Augmented Positional Embeddings (CAPE), by Likhomanenko et al. Enhance your Transformer positional embeddings with easy-to-use augmentations!

Guillermo Cámbara 26 Dec 13, 2022
PyTorch implementation of the ACL, 2021 paper Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks This repo contains the PyTorch implementation of the ACL, 2021 pa

Rabeeh Karimi Mahabadi 98 Dec 28, 2022
VoxHRNet - Whole Brain Segmentation with Full Volume Neural Network

VoxHRNet This is the official implementation of the following paper: Whole Brain Segmentation with Full Volume Neural Network Yeshu Li, Jonathan Cui,

Microsoft 12 Nov 24, 2022
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo

idn-solver Paper | Project Page This repository contains the code release of our ICCV 2021 paper: A Confidence-based Iterative Solver of Depths and Su

zhaowang 43 Nov 17, 2022
Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

Daniel Roich 58 Dec 24, 2022
CoRe: Contrastive Recurrent State-Space Models

CoRe: Contrastive Recurrent State-Space Models This code implements the CoRe model and reproduces experimental results found in Robust Robotic Control

Apple 21 Aug 11, 2022
Official Pytorch implementation of "Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes", CVPR 2022

Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes / 3DCrowdNet News 💪 3DCrowdNet achieves the state-of-the-art accuracy on 3D

Hongsuk Choi 113 Dec 21, 2022
The repository offers the official implementation of our paper in PyTorch.

Cloth Interactive Transformer (CIT) Cloth Interactive Transformer for Virtual Try-On Bin Ren1, Hao Tang1, Fanyang Meng2, Runwei Ding3, Ling Shao4, Phi

Bingoren 49 Dec 01, 2022
Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Fastformer-Keras Unofficial Tensorflow-Keras implementation of Fastformer based on paper Fastformer: Additive Attention Can Be All You Need. Tensorflo

Yam Peleg 10 Jan 30, 2022
Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019)

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019) Introduction Official implementation of Adaptive Pyramid Context Network

21 Nov 09, 2022
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
A transformer which can randomly augment VOC format dataset (both image and bbox) online.

VocAug It is difficult to find a script which can augment VOC-format dataset, especially the bbox. Or find a script needs complex requirements so it i

Coder.AN 1 Mar 05, 2022
Retrieval.pytorch - The code we used in [2020 DIGIX]

Retrieval.pytorch - The code we used in [2020 DIGIX]

Guo-Hua Wang 2 Feb 07, 2022
Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

youceF 1 Nov 12, 2021
This is an official implementation for "AS-MLP: An Axial Shifted MLP Architecture for Vision".

AS-MLP architecture for Image Classification Model Zoo Image Classification on ImageNet-1K Network Resolution Top-1 (%) Params FLOPs Throughput (image

SVIP Lab 106 Dec 12, 2022
End-to-end beat and downbeat tracking in the time domain.

WaveBeat End-to-end beat and downbeat tracking in the time domain. | Paper | Code | Video | Slides | Setup First clone the repo. git clone https://git

Christian J. Steinmetz 60 Dec 24, 2022
Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

zhaohu xing 112 Dec 16, 2022
This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

ICCV Workshop 2021 VTGAN This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers"

Sharif Amit Kamran 25 Dec 08, 2022
Open-World Entity Segmentation

Open-World Entity Segmentation Project Website Lu Qi*, Jason Kuen*, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia This projec

DV Lab 410 Jan 03, 2023