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eess.IV2024★ 3 cited
Characterizing the Interpretability of Attention Maps in Digital Pathology
Tomé Albuquerque, Anil Yüce, Markus D. Herrmann +1
Interpreting machine learning model decisions is crucial for high-risk applications like healthcare. In digital pathology, large whole slide images (WSIs) are decomposed into small…
eess.IV2024
Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks
Victor Ibañez, Przemyslaw Szostak, Quincy Wong +3
Graph convolutional networks (GCNs) have emerged as a powerful alternative to multiple instance learning with convolutional neural networks in digital pathology, offering superior…