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+---
+language:
+- en
+license: apache-2.0
+library_name: atommic
+datasets:
+- SKMTEA
+thumbnail: null
+tags:
+- image-segmentation
+- DynUNet
+- ATOMMIC
+- pytorch
+model-index:
+- name: SEG_DynUNet_SKMTEA
+  results: []
+
+---
+
+
+## Model Overview
+
+AttentionUNet for MRI Segmentation on the SKMTEA dataset.
+
+
+## ATOMMIC: Training
+
+To train, fine-tune, or test the model you will need to install [ATOMMIC](https://github.com/wdika/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](https://github.com/wdika/atommic/tree/main/projects/SEG/SKMTEA/conf).
+
+### Automatically instantiate the model
+
+```base
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_DynUNet_SKMTEA/blob/main/SEG_DynUNet_SKMTEA.atommic
+mode: test
+```
+
+### Usage
+
+You need to download the SKM-TEA dataset to effectively use this model. Check the [SKMTEA](https://github.com/wdika/atommic/blob/main/projects/SEG/SKMTEA/README.md) page for more information.
+
+
+## Model Architecture
+```base
+model:
+  model_name: SEGMENTATIONDYNUNET
+  segmentation_module: DYNUNet
+  segmentation_module_input_channels: 1
+  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: false  # 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
+```base
+  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_find_unused_parameters_false
+  accelerator: gpu
+  devices: 1
+  num_nodes: 1
+  max_epochs: 20
+  precision: 16-mixed  # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+  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](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) script for the segmentation task, with --evaluation_type per_slice.
+
+Results
+-------
+
+Evaluation
+----------
+DICE = 0.6888 +/- 0.1359 F1 = 0.05911 +/- 0.2638 HD95 = 8.973 +/- 4.507 IOU = 0.01517 +/- 0.06638
+
+
+## References
+
+[1] [ATOMMIC](https://github.com/wdika/atommic)
+
+[2] Desai AD, Schmidt AM, Rubin EB, et al. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. 2022