wdika/SEG_UNet_BraTS2023AdultGlioma

Model Overview

AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset.

ATOMMIC: Training

To train, fine-tune, or test the model you will need to install ATOMMIC. We recommend you install it after you've installed latest Pytorch version.

pip install atommic['all']

How to Use this Model

The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Corresponding configuration YAML files can be found here.

Automatically instantiate the model

pretrained: true
checkpoint: https://huggingface.co/wdika/SEG_UNet_BraTS2023AdultGlioma/blob/main/SEG_UNet_BraTS2023AdultGlioma.atommic
mode: test

Usage

You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the BraTS2023AdultGlioma page for more information.

Model Architecture

model:
  model_name: SEGMENTATIONUNET
  segmentation_module: UNet
  segmentation_module_input_channels: 4
  segmentation_module_output_channels: 4
  segmentation_module_channels: 32
  segmentation_module_pooling_layers: 5
  segmentation_module_dropout: 0.0
  segmentation_module_normalize: false
  segmentation_loss:
    dice: 1.0
  dice_loss_include_background: true  # always set to true if the background is removed
  dice_loss_to_onehot_y: false
  dice_loss_sigmoid: false
  dice_loss_softmax: false
  dice_loss_other_act: none
  dice_loss_squared_pred: false
  dice_loss_jaccard: false
  dice_loss_flatten: false
  dice_loss_reduction: mean_batch
  dice_loss_smooth_nr: 1e-5
  dice_loss_smooth_dr: 1e-5
  dice_loss_batch: true
  dice_metric_include_background: true  # always set to true if the background is removed
  dice_metric_to_onehot_y: false
  dice_metric_sigmoid: false
  dice_metric_softmax: false
  dice_metric_other_act: none
  dice_metric_squared_pred: false
  dice_metric_jaccard: false
  dice_metric_flatten: false
  dice_metric_reduction: mean_batch
  dice_metric_smooth_nr: 1e-5
  dice_metric_smooth_dr: 1e-5
  dice_metric_batch: true
  segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
  segmentation_activation: sigmoid
  magnitude_input: true
  log_multiple_modalities: true  # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
  normalization_type: minmax
  normalize_segmentation_output: true
  complex_data: false

Training

  optim:
    name: adam
    lr: 1e-4
    betas:
      - 0.9
      - 0.98
    weight_decay: 0.0
    sched:
      name: InverseSquareRootAnnealing
      min_lr: 0.0
      last_epoch: -1
      warmup_ratio: 0.1

trainer:
  strategy: ddp
  accelerator: gpu
  devices: 1
  num_nodes: 1
  max_epochs: 10
  precision: 16-mixed
  enable_checkpointing: false
  logger: false
  log_every_n_steps: 50
  check_val_every_n_epoch: -1
  max_steps: -1

Performance

Evaluation can be performed using the segmentation evaluation script for the segmentation task, with --evaluation_type per_slice.

Results

Evaluation

DICE = 0.9372 +/- 0.1175 F1 = 0.6713 +/- 0.7867 HD95 = 3.504 +/- 2.089 IOU = 0.5346 +/- 0.6628

Limitations

This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results.

References

[1] ATOMMIC

[2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023