Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations &amp; future potential

Gadermayr, Michael; Tschuchnig, Maximilian

doi:10.1016/j.compmedimag.2024.102337

Cited by 9 publications

(1 citation statement)

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“…To assess the transferability of the pre-training setups we used multiple-instance learning [17] (supplementary section ) to be able to model whole slide images (WSIs). We observed that embeddings generated by the BEVNet pre-trained model translated to a mean 19.2% (see table 1.…”

Section: Modelmentioning

confidence: 99%

Transfer learning may explain pigeons’ ability to detect cancer in histopathology

Kilim,

Báskay,

Biricz

et al. 2024

Bioinspir. Biomim.

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Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade. Could it be that knowledge transferred from their bird's-eye views of the earth's surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we re-capitulate this biological phenomenon with a machine-learning analog. We model pigeons' visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet (BEVNet); our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in the Levenson et al. study, we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin H&E histopathology and radiology images and tasks the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird's-eye view data results in close agreement with pigeon performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image (WSI) data sets representing diverse cancer types.

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Section: Modelmentioning

confidence: 99%