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SpecTr: Spectral Transformer for Microscopic Hyperspectral Pathology Image Segmentation (TCSVT 2023)

Official Code for "SpecTr: Spectral Transformer for Microscopic Hyperspectral Pathology Image Segmentation" by Boxiang Yun, Baiying Lei, Jieneng Chen, Huiyu Wang, Song Qiu, Wei Shen, Qingli Li, Yan Wang*

Introduction

(TCSVT 2023) Official code for "SpecTr: Spectral Transformer for Microscopic Hyperspectral Pathology Image Segmentation". SpecTr

Requirements

This repository is based on PyTorch 1.10, CUDA 11.１, Python 3.９.７, and segmentation-models-pytorch 0.3.3. All experiments in our paper were conducted on NVIDIA GeForce RTX 3090 GPU with an identical experimental setting.

Usage

We provide code, dataset, and model for the MDC dataset.

The official dataset can be found at MDC. However, due to its size, we also provide preprocessed data (including denoising and resizing operations) for reproducing our paper experiments."

Download the dataset and move to the dataset fold.

To train a model,

CUDA_VISIBLE_DEVICES=0 python train_main.py -r ./dataset/MDC -sn 60 -cut 192 -e 75

To test a model,

CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 python evaluate.py -r ./dataset/MDC -sn 60 -cut 192 -name SpecTr_XXXX

Acknowledgements

Some modules in our code were inspired by vit-pytorch and segmentation_models.pytorch. We appreciate the effort of these authors to provide open-source code for the community. Hope our work can also contribute to related research.

Questions

If you encounter any issues accessing the dataset, such as unable to sign in MDC, please contact me at '52265904012@stu.ecnu.edu.cn'