Artifacts from the study "Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype"

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Authors: Jan-Philipp Redlich, Friedrich Feuerhake, Stefan Nikolin, Nadine S. Schaadt, Sarah Teuber‐Hanselmann, J Weis, Sabine Luttmann, Andrea Eberle, Christoph Buck, Timm Intemann, Pascal Birnstill, Klaus Kreywinkel
+3 more Jonas Ort, Peter Böör, André Homeyer
Cited by: 0

Abstract

This record includes artifacts supplementing the study "Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype" (https://doi.org/10.48550/arXiv.2601.11691). Content: example_tiles.pdf: Example tiles decsribed in the study. sparse_autoencoder_checkpoint.pt: Checkpoint of the sparse autoencoder trained for the study. sparse_autoencoder.py: Python script implementing the sparse autoencoder PyTorch class, along with instructions on how to use it. The source code from the study is available at https://github.com/FraunhoferMEVIS/canconnect-study.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Glioma Diagnosis and Treatment AI in cancer detection

Publication details

Journal / source
arXiv (Cornell University)
Publication date
28 January 2026
Language
EN
Document type
Preprint