Weakly supervised medulloblastoma tumor classification using domain specific patch-level feature extraction

Lennart Maack; Debayan Bhattacharya; Finn Behrendt; Michael Bockmayr; Alexander Schlaefer

doi:10.1117/12.3006455

3 April 2024 Weakly supervised medulloblastoma tumor classification using domain specific patch-level feature extraction

Lennart Maack, Debayan Bhattacharya, Finn Behrendt, Michael Bockmayr, Alexander Schlaefer

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129331C (2024) https://doi.org/10.1117/12.3006455
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Medulloblastoma (MB) is the most common embryonal tumour of the brain. In order to decide on an optimal therapy, laborious inspection of histopathological tissue slides by neuropathologists is necessary. Digital pathology with the support of deep learning methods can help to improve the clinical workflow. Due to the high resolution of histopathological images, previous work on MB classification involved manual selection of patches, making it a time consuming task. In order to leverage only slide labels for histopathology image classification, weakly supervised approaches first encode small patches into feature vectors using an ImageNet pretrained encoder based on convolutional neural networks. The representations of patches are further utilized to train a data-efficient attention-based learning method. Due to the domain shift between natural images and histopathology images, the encoder is not optimal for feature extraction for MB classification. In this study, we adapt weakly supervised learning for MB classification and examine different histopathological specific encoder architectures and weights for the MB classification task. The results show that ResNet encoders pretrained with histopathology images lead to better MB classification results compared to encoders pretrained on ImageNet. The best performing method uses a ResNet50 architecture, pretrained on histopathology images and achieves an area under the receiver operating curve (AUROC) value of 71.89%, improving the baseline model by 2%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lennart Maack, Debayan Bhattacharya, Finn Behrendt, Michael Bockmayr, and Alexander Schlaefer "Weakly supervised medulloblastoma tumor classification using domain specific patch-level feature extraction", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129331C (3 April 2024); https://doi.org/10.1117/12.3006455

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