Prof. Heinz Koeppl Profile

Prof. Heinz Koeppl

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Histopathological image classification based on self-supervised vision transformer and weak labels

Ahmet Gokberk Gul, Oezdemir Cetin, Christoph Reich, Nadine Flinner, Tim Prangemeier, Heinz Koeppl

Proceedings Volume 12039, 120391O (2022) https://doi.org/10.1117/12.2624609

KEYWORDS: Transformers, Computer programming, Tissues, Image resolution, Image classification, Tumor growth modeling, Image processing, Image segmentation

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Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer in tissue samples. Automating this diagnosis poses various issues, most notably caused by the immense image resolution and limited annotations. WSI’s commonly exhibit resolutions of 100, 000 × 100, 000 pixels. Annotating cancerous areas in WSI’s on the pixel-level is prohibitively labor-intensive and requires a high level of expert knowledge. Multiple instance learning (MIL) alleviates the need for expensive pixel-level annotations. In MIL, learning is performed on slide-level labels, in which a pathologist provides information about whether a slide includes cancerous tissue. Here, we propose Self-ViT-MIL, a novel approach for classifying and localizing cancerous areas based on slide-level annotations, eliminating the need for pixel-wise annotated training data. Self-ViTMIL is pre-trained in a self-supervised setting to learn rich feature representation without relying on any labels. The recent Vision Transformer (ViT) architecture builds the feature extractor of Self-ViT-MIL. For localizing cancerous regions, a MIL aggregator with global attention is utilized. To the best of our knowledge, Self-ViTMIL is the first approach to introduce self-supervised ViT’s in MIL-based WSI analysis tasks. We showcase the effectiveness of our approach on the common Camelyon16 dataset. Self-ViT-MIL surpasses existing stateof-the-art MIL-based approaches in terms of accuracy and area under the curve (AUC). Code is available at https://github.com/gokberkgul/self-learning-transformer-mil

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