rezazad68 / TMUnet

Contextual Attention Network: Transformer Meets U-Net

Contextual Attention Network: Transformer Meets U-Net

Contexual attention network for medical image segmentation with state of the art results on skin lesion segmentation, multiple myeloma cell segmentation. This method incorpotrates the transformer module into a U-Net structure so as to concomitantly capture long-range dependency along with resplendent local informations. If this code helps with your research please consider citing the following paper:

R. Azad, Moein Heidari, Yuli Wu and Dorit Merhof , "Contextual Attention Network: Transformer Meets U-Net", download link.

@article{reza2022contextual,
  title={Contextual Attention Network: Transformer Meets U-Net},
  author={Reza, Azad and Moein, Heidari and Yuli, Wu and Dorit, Merhof},
  journal={arXiv preprint arXiv:2203.01932},
  year={2022}
}

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Updates

• February 27, 2022: First release (Complete implemenation for SKin Lesion Segmentation on ISIC 2017, SKin Lesion Segmentation on ISIC 2018, SKin Lesion Segmentation on PH2 and Multiple Myeloma Cell Segmentation (SegPC 2021) dataset added.)

This code has been implemented in python language using Pytorch library and tested in ubuntu OS, though should be compatible with related environment. following Environement and Library needed to run the code:

• Python 3
• Pytorch

Run Demo

For training deep model and evaluating on each data set follow the bellow steps:
1- Download the ISIC 2018 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18.
2- Run Prepare_ISIC2018.py for data preperation and dividing data to train,validation and test sets.
3- Run train_skin.py for training the model using trainng and validation sets. The model will be train for 100 epochs and it will save the best weights for the valiation set.
4- For performance calculation and producing segmentation result, run evaluate_skin.py. It will represent performance measures and will saves related results in results folder.

Notice: For training and evaluating on ISIC 2017 and ph2 follow the bellow steps :

ISIC 2017- Download the ISIC 2017 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18\7.
then Run Prepare_ISIC2017.py for data preperation and dividing data to train,validation and test sets.
ph2- Download the ph2 dataset from this link and extract it then Run Prepare_ph2.py for data preperation and dividing data to train,validation and test sets.
Follow step 3 and 4 for model traing and performance estimation. For ph2 dataset you need to first train the model with ISIC 2017 data set and then fine-tune the trained model using ph2 dataset.

Quick Overview

Perceptual visualization of the proposed Contextual Attention module.

Results

For evaluating the performance of the proposed method, Two challenging task in medical image segmentaion has been considered. In bellow, results of the proposed approach illustrated.

Task 1: SKin Lesion Segmentation

Performance Comparision on SKin Lesion Segmentation

In order to compare the proposed method with state of the art appraoches on SKin Lesion Segmentation, we considered Drive dataset.

Methods (On ISIC 2017)	Dice-Score	Sensivity	Specificaty	Accuracy
Ronneberger and et. all U-net	0.8159	0.8172	0.9680	0.9164
Oktay et. all Attention U-net	0.8082	0.7998	0.9776	0.9145
Lei et. all DAGAN	0.8425	0.8363	0.9716	0.9304
Chen et. all TransU-net	0.8123	0.8263	0.9577	0.9207
Asadi et. all MCGU-Net	0.8927	0.8502	0.9855	0.9570
Valanarasu et. all MedT	0.8037	0.8064	0.9546	0.9090
Wu et. all FAT-Net	0.8500	0.8392	0.9725	0.9326
Azad et. all Proposed TMUnet	0.9164	0.9128	0.9789	0.9660

For more results on ISIC 2018 and PH2 dataset, please refer to the paper

SKin Lesion Segmentation segmentation result on test data

(a) Input images. (b) Ground truth. (c) U-net. (d) Gated Axial-Attention. (e) Proposed method without a contextual attention module and (f) Proposed method.

Multiple Myeloma Cell Segmentation

Performance Evalution on the Multiple Myeloma Cell Segmentation task

Methods	mIOU
Frequency recalibration U-Net	0.9392
XLAB Insights	0.9360
DSC-IITISM	0.9356
Multi-scale attention deeplabv3+	0.9065
U-Net	0.7665
Baseline	0.9172
Proposed	0.9395

Multiple Myeloma Cell Segmentation results

Model weights

You can download the learned weights for each dataset in the following table.

Dataset	Learned weights
ISIC 2018	TMUnet
ISIC 2017	TMUnet
Ph2	TMUnet

Query

All implementations are done by Reza Azad and Moein Heidari. For any query please contact us for more information.

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