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15 February 2021 Few-shot weakly supervised detection and retrieval in histopathology whole-slide images
Mart van Rijthoven, Maschenka Balkenhol, Manfredo Atzori, Peter Bult, Jeroen van der Laak, Francesco Ciompi
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Abstract
In this work, we propose a deep learning system for weakly supervised object detection in digital pathology whole slide images. We designed the system to be organ- and object-agnostic, and to be adapted on-the-fly to detect novel objects based on a few examples provided by the user. We tested our method on detection of healthy glands in colon biopsies and ductal carcinoma in situ (DCIS) of the breast, showing that (1) the same system is capable of adapting to detect requested objects with high accuracy, namely 87% accuracy assessed on 582 detections in colon tissue, and 93% accuracy assessed on 163 DCIS detections in breast tissue; (2) in some settings, the system is capable of retrieving similar cases with little to none false positives (i.e., precision equal to 1.00); (3) the performance of the system can benefit from previously detected objects with high confidence that can be reused in new searches in an iterative fashion.
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Mart van Rijthoven, Maschenka Balkenhol, Manfredo Atzori, Peter Bult, Jeroen van der Laak, and Francesco Ciompi "Few-shot weakly supervised detection and retrieval in histopathology whole-slide images", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 116030N (15 February 2021); https://doi.org/10.1117/12.2582132
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KEYWORDS
Breast

Colon

Tissues

Biopsy

Pathology

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