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_DynUNet_BraTS2023AdultGlioma/blob/main/SEG_DynUNet_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: SEGMENTATIONDYNUNET
  segmentation_module: DYNUNet
  segmentation_module_input_channels: 4
  segmentation_module_output_channels: 4
  segmentation_module_channels:
    - 32
    - 64
    - 128
    - 256
    - 512
  segmentation_module_kernel_size:
    - 3
    - 3
    - 3
    - 3
    - 1
  segmentation_module_strides:
    - 1
    - 1
    - 1
    - 1
    - 1
  segmentation_module_dropout: 0.0
  segmentation_module_norm: instance
  segmentation_module_activation: leakyrelu
  segmentation_module_deep_supervision: true
  segmentation_module_deep_supervision_levels: 2
  segmentation_module_normalize: false
  segmentation_module_norm_groups: 2
  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 = 0.8061 +/- 0.276 F1 = 0.1045 +/- 0.5801 HD95 = 5.119 +/- 5.411 IOU = 0.06959 +/- 0.4187

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

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The HF Inference API does not support image-segmentation models for atommic library.