MultiPersistence Topological Fusion with Vision Transformers for Skin Cancer Detection

Fulya Tastan, Sayoni Chakraborty, Sangyeon Lee, Baris Coskunuzer
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:2071-2096, 2026.

Abstract

Skin cancer is a common and potentially fatal disease where early detection is crucial, especially for melanoma. Current deep learning systems classify skin lesions well, but they primarily rely on appearance cues and may miss deeper structural patterns in lesions. We present TopoCon-MP, a method that extracts multiparameter topological signatures from dermoscopic images to capture multiscale lesion structure, and fuses these signatures with Vision Transformers using a supervised contrastive objective. Across three public datasets, TopoCon-MP improves in-distribution performance over strong pretrained CNN and ViT baselines, and in cross-dataset transfer, it maintains competitive performance. Ablations show that both multiparameter topology and contrastive fusion contribute to these gains. The resulting topological channels also provide an interpretable view of lesion organization that aligns with clinically meaningful structures. Overall, TopoCon-MP demonstrates that multipersistence-based topology can serve as a complementary modality for more robust skin cancer detection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-tastan26a, title = {MultiPersistence Topological Fusion with Vision Transformers for Skin Cancer Detection}, author = {Tastan, Fulya and Chakraborty, Sayoni and Lee, Sangyeon and Coskunuzer, Baris}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {2071--2096}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/tastan26a/tastan26a.pdf}, url = {https://proceedings.mlr.press/v315/tastan26a.html}, abstract = {Skin cancer is a common and potentially fatal disease where early detection is crucial, especially for melanoma. Current deep learning systems classify skin lesions well, but they primarily rely on appearance cues and may miss deeper structural patterns in lesions. We present TopoCon-MP, a method that extracts multiparameter topological signatures from dermoscopic images to capture multiscale lesion structure, and fuses these signatures with Vision Transformers using a supervised contrastive objective. Across three public datasets, TopoCon-MP improves in-distribution performance over strong pretrained CNN and ViT baselines, and in cross-dataset transfer, it maintains competitive performance. Ablations show that both multiparameter topology and contrastive fusion contribute to these gains. The resulting topological channels also provide an interpretable view of lesion organization that aligns with clinically meaningful structures. Overall, TopoCon-MP demonstrates that multipersistence-based topology can serve as a complementary modality for more robust skin cancer detection.} }
Endnote
%0 Conference Paper %T MultiPersistence Topological Fusion with Vision Transformers for Skin Cancer Detection %A Fulya Tastan %A Sayoni Chakraborty %A Sangyeon Lee %A Baris Coskunuzer %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-tastan26a %I PMLR %P 2071--2096 %U https://proceedings.mlr.press/v315/tastan26a.html %V 315 %X Skin cancer is a common and potentially fatal disease where early detection is crucial, especially for melanoma. Current deep learning systems classify skin lesions well, but they primarily rely on appearance cues and may miss deeper structural patterns in lesions. We present TopoCon-MP, a method that extracts multiparameter topological signatures from dermoscopic images to capture multiscale lesion structure, and fuses these signatures with Vision Transformers using a supervised contrastive objective. Across three public datasets, TopoCon-MP improves in-distribution performance over strong pretrained CNN and ViT baselines, and in cross-dataset transfer, it maintains competitive performance. Ablations show that both multiparameter topology and contrastive fusion contribute to these gains. The resulting topological channels also provide an interpretable view of lesion organization that aligns with clinically meaningful structures. Overall, TopoCon-MP demonstrates that multipersistence-based topology can serve as a complementary modality for more robust skin cancer detection.
APA
Tastan, F., Chakraborty, S., Lee, S. & Coskunuzer, B.. (2026). MultiPersistence Topological Fusion with Vision Transformers for Skin Cancer Detection. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:2071-2096 Available from https://proceedings.mlr.press/v315/tastan26a.html.

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