From the 1 of 5 linked papers with an AI index.
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Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Sweta Banerjee, Alireza Teimoury, Nils Porsche +11
The paper evaluates whether pathology foundation models can serve as effective backbones for dense detection of mitotic figures, comparing several self‑supervised models to a ResNe…
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation
Sweta Banerjee, Viktoria Weiss, Taryn A. Donovan +9
Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. However, atypical mitosis classificati…
SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces
Sweta Banerjee, Timo Gosch, Sara Hester +11
The annotation of large scale histopathology image datasets remains a major bottleneck in developing robust deep learning models for clinically relevant tasks, such as mitotic figu…
Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
Christof A. Bertram, Viktoria Weiss, Taryn A. Donovan +6
Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has be…
On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images
Jonathan Ganz, Christian Marzahl, Jonas Ammeling +20
The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However…