faizan1234567/brats23-tumors-segmentation

Brain tumors segmentation on 3D MRI images. The model has been trained on BratTS20 and BraTS21 datasets, and now working with BraTS23.

I specialize in Deep Learning, Robotics, and Embedded Software. I'm passionate about vision-language models, medical image analysis, large language models, and robot learning.

Property	Data
Languages
Domain Knowledge
CI / CD
OS
Tools & Platform
Artificial Intelligence
Education

Feel free to connect with me! I'm always open to engaging in exciting opportunities and insightful discussions. Let's connect and collaborate!

brats23-tumors-segmentation's Issues

Expected performance

Thanks for the repo! Do you have expected algorithm outputs for when we use or don't use the pretrained weights? Also how did you pretrain the model considering a large portion of the 2021 and 2023 dataset overlap (as far as I know)?

training

how to train a model?

RuntimeError: CUDA error: out of memory

I am constantly getting runtime error whenever the validation step reaches. I have tried to run it in google colab as well as in my local computer with nvidia rtx3090. what could be the reason? any idea? thank you.

Detailed description
Val 0/4 0/250 , dice_tc: 1.0353405 , dice_wt: 0.96759117 , dice_et: 1.3043995 , time 6.37s
Val 0/4 1/250 , dice_tc: 1.0755885 , dice_wt: 1.1260056 , dice_et: 1.3453007 , time 3.67s
Error executing job with overrides: []
Traceback (most recent call last):
File "/home/navi/Brats-20-Tumors-segmentation/train.py", line 478, in main
run(args, model=model,
File "/home/navi/Brats-20-Tumors-segmentation/train.py", line 358, in run
) = trainer(
File "/home/navi/Brats-20-Tumors-segmentation/train.py", line 249, in trainer
val_acc = val(model= model,
File "/home/navi/Brats-20-Tumors-segmentation/train.py", line 138, in val
for index, batch_data in enumerate(loader):
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in next
data = self._next_data()
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1346, in _next_data
return self._process_data(data)
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1372, in _process_data
data.reraise()
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/_utils.py", line 722, in reraise
raise exception
RuntimeError: Caught RuntimeError in pin memory thread for device 0.
Original Traceback (most recent call last):
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 36, in do_one_step
data = pin_memory(data, device)
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 62, in pin_memory
return type(data)({k: pin_memory(sample, device) for k, sample in data.items()}) # type: ignore[call-arg]
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 62, in
return type(data)({k: pin_memory(sample, device) for k, sample in data.items()}) # type: ignore[call-arg]
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/utils/data/_utils/pin_memory.py", line 57, in pin_memory
return data.pin_memory(device)
File "/home/navi/MONAI/monai/data/meta_tensor.py", line 282, in torch_function
ret = super().torch_function(func, types, args, kwargs)
File "/home/navi/miniconda3/envs/myenv/lib/python3.10/site-packages/torch/_tensor.py", line 1418, in torch_function
ret = func(*args, **kwargs)
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Steps to reproduce

1. Set all the directories correctly in the configuration file.
2. Run python train.py

MASK

whats mask in this dataset? can u explain more?

clarification regarding JSON File. " " --json_file JSON_FILE path to json file""

Hi, Thank you for your reply. I have one more question related to configurations. Can you elaborate a bit on " --json_file JSON_FILE path to json file". What kind of data does it contain? Is it the folds data to train the model? If possible can you please add it to the repository for BraTS23 dataset? Thank you

PreTrained Weights

Hi Faizan, Thanks for making available this amazing work to everyone on github. I'm trying to use this model to obtain the brain tumor segmentation which I would be using for the downstream tasks. I currently have very limited computational resources to actually train the model and use it for inference. It would be really helpful if you could provide the pretrained model and its weights. I will make sure to give credits to this work if I use it for publication. thank you