Abstract
Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no “gold-standard” independent ground-truth. However, it is well-established that these interpretation based labels are often unreliable, in part, due to differences in expertise levels and human subjectivity. In this paper, our goal is to shed light on the inherent uncertainty of mitosis labels and characterize the mitotic figure classification task in a human interpretable manner. We train a probabilistic diffusion model to synthesize patches of cell nuclei for a given mitosis label condition. Using this model, we can then generate a sequence of synthetic images that correspond to the same nucleus transitioning into the mitotic state. This allows us to identify different image features associated with mitosis, such as cytoplasm granularity, nuclear density, nuclear irregularity and high contrast between the nucleus and the cell body. Our approach offers a new tool for pathologists to interpret and communicate the features driving the decision to recognize a mitotic figure.
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Acknowledgements
Funding for this project was partially provided by The New York-Presbyterian Hospital William Rhodes Center for Glioblastoma-Collaborative Research Initiative, a Weill Cornell Medicine Neurosurgery-Cornell Biomedical Engineering seed grant, The Burroughs Wellcome Weill Cornell Physician Scientist Program Award, NIH grant R01AG053949, and the NSF CAREER 1748377 grant. Project support for this study was provided by the Center for Translational Pathology of the Department of Pathology and Laboratory Medicine at Weill Cornell Medicine.
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Bahadir, C.D., Liechty, B., Pisapia, D.J., Sabuncu, M.R. (2024). Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds) Deep Generative Models. MICCAI 2023. Lecture Notes in Computer Science, vol 14533. Springer, Cham. https://doi.org/10.1007/978-3-031-53767-7_12
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