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Seg Attentionunet Isles2022subacutestroke dataset by wdika
⭐ 25.6
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Dataset

Seg Attentionunet Isles2022subacutestroke

by wdika hf-model--wdika--seg_attentionunet_isles2022subacutestroke
Nexus Index
25.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 23
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 25.6 FNI Score
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- Rows
Parquet Format
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Dataset Information Summary
Entity Passport
Registry ID hf-model--wdika--seg_attentionunet_isles2022subacutestroke
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_model__wdika__seg_attentionunet_isles2022subacutestroke,
  author = {wdika},
  title = {Seg Attentionunet Isles2022subacutestroke Dataset},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/dataset/hf-model--wdika--seg_attentionunet_isles2022subacutestroke}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
wdika. (2026). Seg Attentionunet Isles2022subacutestroke [Dataset]. Free2AITools. https://free2aitools.com/dataset/hf-model--wdika--seg_attentionunet_isles2022subacutestroke

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
25.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 23

đŸ’Ŧ Index Insight

FNI V2.0 for Seg Attentionunet Isles2022subacutestroke: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:23).

Free2AITools Nexus Index

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Dataset Specification

Model Overview

AttentionUNet for MRI Segmentation on the ISLES2022SubAcuteStroke 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.

text 
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

base 
pretrained: true
checkpoint: https://huggingface.co/wdika/SEG_AttentionUNet_ISLES2022SubAcuteStroke/blob/main/SEG_AttentionUNet_ISLES2022SubAcuteStroke.atommic
mode: test

Usage

You need to download the ISLES 2022 Sub Acute Stroke dataset to effectively use this model. Check the ISLES2022SubAcuteStroke page for more information.

Model Architecture

base 
model:
  model_name: SEGMENTATIONATTENTIONUNET
  segmentation_module: AttentionUNet
  segmentation_module_input_channels: 3
  segmentation_module_output_channels: 1
  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 ]
  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

base 
  optim:
    name: adamw
    lr: 1e-4
    betas:
      - 0.9
      - 0.999
    weight_decay: 0.0
    sched:
        name: CosineAnnealing
        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: 50
  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 script for the segmentation task, with --evaluation_type per_slice.

Results

Evaluation

ALD = 0.8087 +/- 2.407 AVD = 0.5476 +/- 3.411 DICE = 0.7092 +/- 0.5525 L-F1 = 0.7986 +/- 0.579

Limitations

This model was trained on the ISLES2022SubAcuteStroke dataset with stacked ADC, DWI, FLAIR images and might differ in performance compared to the leaderboard results.

References

[1] ATOMMIC

[2] Petzsche MRH, Rosa E de la, Hanning U, et al. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data 1 2022;9

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Technical metadata sourced from upstream repositories.

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🆔 Identity & Source

id
hf-model--wdika--seg_attentionunet_isles2022subacutestroke
slug
wdika--seg_attentionunet_isles2022subacutestroke
source
huggingface
author
wdika
license
tags

⚙ī¸ Technical Specs

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params billions
null
context length
null
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