Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ThirteenYue/2MSPK-Net

Last synced: about 1 month ago
JSON representation

Host: GitHub
URL: https://github.com/ThirteenYue/2MSPK-Net
Owner: ThirteenYue
Created: 2024-03-19T09:12:36.000Z (10 months ago)
Default Branch: master
Last Pushed: 2024-05-23T09:03:49.000Z (8 months ago)
Last Synced: 2024-05-23T10:25:33.615Z (8 months ago)
Language: Python
Size: 1.97 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

Awesome-Segment-Anything - [code

README

# 2MSPK-Net
This repo is the official implementation of
['2MSPK-Net: A Nuclei Segmentation Network Based on Multi-Scale, Multi-Dimensional Attention, and SAM Prior Knowledge']

We proposed a segmentation method based on SAM prior knowledge guidance strategy, and the above is a schematic diagram of integrating SAM prior knowledge.For detailed method introduction, please read the original article

## Requirements
Install from the ```requirements.txt``` using:
```angular2html
pip install -r requirements.txt
```

## Usage

*Note: If you have some problems with the code, the [issues](https://github.com/ThirteenYue/2MSPK-Net/issues) may help.*

### 1. Data Preparation
#### 1.1. GlaS and MoNuSeg Datasets
🔥 The original data can be downloaded in following links:
* MoNuSeg Dataset - [Link (Original)](https://monuseg.grand-challenge.org/Data/)
* TNBC Dataset - [Link (Original)](https://paperswithcode.com/dataset/tnbc)

Then prepare the datasets in the following format for easy use of the code:
```angular2html
├── Dataset
   ├── MoNusg
   │   ├── test
   │   │   ├── boundary_priors
│   │   ├── images
│   │   ├── masks
   │   │   └── seg_priors
│   ├── train
   │   │   ├── boundary_priors
│   │   ├── images
│   │   ├── masks
   │   │   └── seg_priors
   │   └── val
   │   │   ├── boundary_priors
│   │   ├── images
│   │   ├── masks
         └── seg_priors
```

### 2. Training
During the training process, the data were uniformly resized to $256\times256$ pixels and data augmentation was applied, including affine transformation, random flipping, and random rotation. Gradient descent was performed using the Adam optimizer with $\beta_1$ set to 0.9 and $\beta_2$ set to 0.999. The initial learning rate was set to $1\times{10}^{-4}$, and an adaptive learning rate decay strategy was employed. If the loss on the validation set did not decrease after every 20 epochs, the learning rate was reduced by a factor of 0.5. The batch size was set to 4, and the training was completed after 600 epochs.

#### 2.2 Pre-training
We didn't use any pre-trained weights

### 3. Testing
We also added SAM prior area maps and contour maps to the test data set
* You can generate area maps and contour mapsarea and contour plots using [SAM_prior](https://github.com/ThirteenYue/2MSPK-Net/blob/master/src/SAM_prior.py)
We will announce other test and visualization codes later

## Reference
* UNet:https://github.com/LeeJunHyun/Image_Segmentation#u-net
* UNet++: https://github.com/qubvel/segmentation_models.pytorch
* U2Net:https://github.com/huangmozhilv/u2net_torch
* Attention U-Net: https://github.com/bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets
* TransUNet: https://github.com/Beckschen/TransUNet
* Swin-Unet: https://github.com/HuCaoFighting/Swin-Unet

## Contact
Weight files and evaluation codes can be obtained by contacting the author
——Gongtao Yue([[email protected]]([email protected]))