OncoTUM models

Version 1.0
Suditsch, Marlon; Wagner, Arndt; Ricken, Tim, 2024, "OncoTUM models", https://doi.org/10.18419/DARUS-4647, DaRUS, V1
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Description

OncoTUM models

This repository hosts pretrained neural network models for OncoTUM, a key software package within the umbrella project Onco* for modelling and numerical simulations of tumours. OncoTUM is designed to facilitate tumour segmentations from medical images, leveraging state-of-the-art deep learning techniques.

Purpose

The pretrained models in this repository are required for using the inference function of OncoTUM. These models have been trained on relevant datasets (BraTS 2020) to ensure high accuracy and performance in tumor segmentation and its classification.

Usage

To utilise the inference functionality of OncoTUM, download the appropriate pretrained models from this repository and ensure they are correctly linked to the OncoTUM software package. Detailed instructions for setup and integration can be found in the OncoTUM documentation.

Content

In order to remain with most possible flexibility, the modality agnostic implementation allows to perform segmentation with a subset of the gold standard modalities.

Brain tumour segmentation

  • Full modal model: trained to all gold standard modalities (t1, t1gd, t2, flair).
  • Single modality model: trained to single modalities of the gold standard.
  • Null image: Empty image for training.
(2024-12-12)
Subject Engineering
Keyword Neural Networks, Artificial Neural Network, Tumour, Image Segmentation
License/Data Use Agreement

GPL 3.0 or later

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0_im.nii.gz
data/Gzip Archive - 34.0 KB
Published Dec 16, 2024
13 Downloads
MD5: 91ce61bafac37076f37555d587cbb713
hyperparam.yaml
data/flair/Unknown - 417 B
Published Dec 16, 2024
13 Downloads
MD5: d3679f6182bb1172a348f662868f2f6a
model.txt
data/flair/Plain Text - 5.0 KB
Published Dec 16, 2024
14 Downloads
MD5: eee9cd1e68fd7f7c09297b435556a910
Preview "data/flair/model.txt"
model_best.pth.tar
data/flair/TAR Archive - 265.0 MB
Published Dec 16, 2024
13 Downloads
MD5: a772e70dd2bf9faf63b1b699d702bc35
hyperparam.yaml
data/full/Unknown - 437 B
Published Dec 16, 2024
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MD5: 660c4d380a690f173e7315ba7121b3d8
model.txt
data/full/Plain Text - 5.0 KB
Published Dec 16, 2024
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MD5: 0f85e5d4ecf10b0ca1624474af24da13
Preview "data/full/model.txt"
model_best.pth.tar
data/full/TAR Archive - 265.0 MB
Published Dec 16, 2024
11 Downloads
MD5: 9d6fc08ea640bb69d0772902e3b3f5af
hyperparam.yaml
data/t1/Unknown - 414 B
Published Dec 16, 2024
13 Downloads
MD5: 8dc9f2e10718da751209f15ff23ab3a9
model_best.pth.tar
data/t1/TAR Archive - 265.0 MB
Published Dec 16, 2024
12 Downloads
MD5: 372a5847acaad7f34192d7c9189ce8f2
hyperparam.yaml
data/t1gd/Unknown - 416 B
Published Dec 16, 2024
14 Downloads
MD5: 16d97c4a2f33b4c17d2f3af41a8b87eb
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