Code for Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

Himashi Amanda Peiris

Last update: Sep 15, 2022

Related tags

Deep Learning adversarial-learning brain-tumor-segmentation virtual-adversarial-training pytorch-implementation discriminator-model brats2021

Overview

BRATS 2021 Solution For Segmentation Task

This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task.

Environment

Prepare an environment with python=3.8, and then run the command "pip install -r requirements.txt" for the dependencies.

Data Preparation

File structure

 BRATS2021
  |---Data
  |   |--- RSNA_ASNR_MICCAI_BraTS2021_TrainingData
  |   |   |--- BraTS2021_00000
  |   |   |   |--- BraTS2021_00000_flair...
  |   
  |              
  |   
  |
  |---train.py
  |---test.py
  ...

Train/Test

Train : Run the train script on BraTS 2021 Training Dataset with Base model Configurations.

python train.py --num_classes 3 --epochs 350

Test : Run the test script on BraTS 2021 Training Dataset.

python test.py --num_classes 3

Acknowledgements

This repository makes liberal use of code from open_brats2020.

References

BraTS 2021

Citing our work

   @misc{peiris2022reciprocal,
      title={Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task}, 
      author={Himashi Peiris and Zhaolin Chen and Gary Egan and Mehrtash Harandi},
      year={2022},
      eprint={2201.03777},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
   }

You might also like...

Meli Data Challenge 2021 - First Place Solution

My solution for the Meli Data Challenge 2021

23 Mar 9, 2022

The sixth place winning solution (6/220) in 2021 Gaofen Challenge.

SwinTransformer + OBBDet The sixth place winning solution (6/220) in the track of Fine-grained Object Recognition in High-Resolution Optical Images, 2

46 Dec 2, 2022

Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge.

KAIROS MineRL BASALT Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL B

37 Oct 30, 2022

1st place solution in CCF BDCI 2021 ULSEG challenge

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

30 Nov 22, 2022

Code for 1st place solution in Sleep AI Challenge SNU Hospital

Sleep AI Challenge SNU Hospital 2021 Code for 1st place solution for Sleep AI Challenge (Note that the code is not fully organized) Refer to the notio

13 Jan 3, 2022

My personal code and solution to the Synacor Challenge from 2012 OSCON.

Synacor OSCON Challenge Solution (2012) This repository contains my code and solution to solve the Synacor OSCON 2012 Challenge. If you are interested

2 Mar 20, 2022

PyTorch Implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedding (ORAL, MICCAIW 2021)

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding PyTorch implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedd

22 Oct 21, 2022

2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.

TableMASTER-mmocr Contents About The Project Method Description Dependency Getting Started Prerequisites Installation Usage Data preprocess Train Infe

298 Dec 21, 2022

1st Solution For NeurIPS 2021 Competition on ML4CO Dual Task

KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a

24 Sep 8, 2022

Comments

test

Hello, author, I run according to your test.py. The following problems occur. What is the reason? We look forward to you.

Working with 1 GPUs invalid literal for int() with base 10: '' using default group number [24, 48, 96, 192] total number of trainable parameters 4793895 Validation Input Directory /input Bench Test dataset number of batch: 1 => loading checkpoint runs/brats_2021/model_1/model_best.pth.tar => loaded checkpoint 'runs/brats_2021/model_1/model_best.pth.tar' (epoch 3) Traceback (most recent call last): File "test.py", line 117, in main(arguments) File "test.py", line 111, in main generate_segmentations(bench_loader, model_1, args) File "test.py", line 40, in generate_segmentations for i, batch in enumerate(data_loader): File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 530, in next data = self._next_data() File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data return self._process_data(data) File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data data.reraise() File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/_utils.py", line 457, in reraise raise exception RuntimeError: Caught RuntimeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/media/hkw/hkwdisk/viv-test/vizviva_brats_2021-main (1)/vizviva_brats_2021-main/dataset/brats.py", line 39, in getitem patient_image = {key: self.load_nii(_patient[key]) for key in _patient if key not in ["id", "seg"]} File "/media/hkw/hkwdisk/viv-test/vizviva_brats_2021-main (1)/vizviva_brats_2021-main/dataset/brats.py", line 39, in patient_image = {key: self.load_nii(_patient[key]) for key in _patient if key not in ["id", "seg"]} File "/media/hkw/hkwdisk/viv-test/vizviva_brats_2021-main (1)/vizviva_brats_2021-main/dataset/brats.py", line 67, in load_nii return sitk.GetArrayFromImage(sitk.ReadImage(str(path_folder))) File "/home/hkw/anaconda3/envs/pytorch/lib/python3.7/site-packages/SimpleITK/SimpleITK.py", line 8876, in ReadImage return _SimpleITK.ReadImage(*args) RuntimeError: Exception thrown in SimpleITK ReadImage: /tmp/SimpleITK/Code/IO/src/sitkImageReaderBase.cxx:99: sitk::ERROR: The file "/media/hkw/hkwdisk/viv-test/vizviva_brats_2021-main (1)/vizviva_brats_2021-main/BRATS2021/Data/valid/BraTS2021_00046_t1.nii.gz" does not exist.

opened by H-CODE6 32
Couldn't understand how to test on one instance

Hello i was trying to implement this model on a small application im having two problems: 1- is there a function that i d give the model and the niftii of one patient or the multiple niftii of one patient and it returns a segmented nifti something like test( OneData,Model) : segmentation or isn't there anything like that in this git. 2- Can this run on cpu only or do i need a gpu

Thanks in advance Great work

opened by JustAnotherNerdHeHeHe 8
performance

Hello, the author, recently reading your vizviva_ Brats_ 2021 code, found in brats_ When validating in the train, add the patient as the above training code_ Image, patient_ Label = pad_ Or_ Crop_ Image (patient_image, patient\u label, target\u size= (128, 128, 128)), I found that the performance decreased a lot. I don't know why.

opened by H-CODE6 6
Pretrained Model

Hello, first of all, great job. I am trying to use this model and implement it in an app I'm working on. I can't find the pre-trained model if you didn't share it yet is there anyway you can share it with me. My computer isn't strong enough to run the training. Thanks in advance.

opened by KarikalStrom 2

Owner

Himashi Amanda Peiris

Former Senior Software Engineer at Pearson. Currently a PhD Candidate in Monash University

GitHub

This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer This repo is the official implementation for TransBTS: Multimodal Brain Tumor Segmenta

247 Dec 28, 2022

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks