AI for Breast Cancer using Histopathology Images

Abstract:

Computational pathology using AI techniques for breast cancer. In the following works, we perform:

• Segmentation [1]

• CNNs, ViTs, knowledge distillation models [2, 3]

We test the performance of these AI models on publicly available histopathology datasets.

References:

[1] V. B. S. Prasath, F. Bunyak, P. Dale, S. R. Frazier, K. Palaniappan. Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology. First IEEE Healthcare Technology Conference: Translational Engineering in Health & Medicine (IEEE HIC 2012), Houston, TX, USA.

[2] S. Boudissa, S. S. Debsarkar, H. Kawanaka, B. Aronow, V. B. S. Prasath. Vision transformers and CNN-based knowledge-distillation for histopathological image classification. 10th International Conference on Fuzzy Systems and Data Mining (FSDM), Matsue, Japan, November 2024. Proc. IoS Press, 231-239, December 2024. doi:10.3233/FAIA241423

[3] S. Boudissaa, S. S. Debsarkar, H. Kawanaka, B. Aronow, V. B. S. Prasath. Vision transformers for histopathological image classification with efficient head pruning. Submitted, 2025. 

[4] S. Boudissa, S. S. Debsarkar, H. Kawanaka, B. J. Aronow, V. B. S. Prasath. Anonymous Submission. Submitted, 2025.

In preparation:

Segmentation of breast cancer tissue microarrays for stromal-epithelial separation in pathology.

State of the art in digital histopathology image processing: Breast cancer.

Deep learning knowledge-distillation for histopathological image classification.

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