End-to-end learning for detecting MYC translocations

Stephan Dooper, Geert Litjens

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Recent developments have improved whole-slide image classification to the point where the entire slide can be analyzed using only weak labels, whilst retaining both local and global context. In this paper, we use an end-to-end whole-slide image classification approach using weak labels to classify MYC translocations in slides of diffuse large B-cell lymphoma. Our model is able to achieve an AUC of 0.8012, which indicates the possibility of learning relevant features for MYC translocations.
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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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