\documentclass{midl}  
\jmlrvolume{-- 170}
\jmlryear{2026}
\jmlrworkshop{Full Paper -- MIDL 2026}
\editors{Accepted for publication at MIDL 2026}






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\title[Skin Cancer Diagnosis with Topological Vision Transformers]{MultiPersistence Topological Fusion with\\ Vision Transformers for Skin Cancer Detection}



%More complicate cases, e.g. with dual affiliations and joint authorship
\midlauthor{\Name{Fulya Tastan\midljointauthortext{Contributed equally}\nametag{$^{1}$}}  \Email{ftastan@ucsc.edu}\\
\Name{Sayoni Chakraborty\midlotherjointauthor\nametag{$^{2}$}} \Email{sayoni.chakraborty@utdallas.edu}\\
\Name{Sangyeon Lee\midlotherjointauthor\nametag{$^{2}$}} \Email{sangyeon.lee@utdallas.edu}\\
\Name{Baris Coskunuzer\nametag{$^{2}$}} \Email{coskunuz@utdallas.edu}\\
\addr $^{1}$ UC Santa Cruz, Mathematics Department, Santa Cruz, CA 95064 USA\\
\addr $^{2}$ UT Dallas, Department of Mathematical Sciences, Richardson, TX 75080 USA
}

\begin{document}

\maketitle

\begin{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.
\end{abstract}

\begin{keywords}
Skin lesion classification, dermoscopy, medical image analysis, cubical multiparameter persistence, topological data analysis, supervised contrastive learning\end{keywords}


\input{sections/1-intro}

\input{sections/2-background}

\input{sections/3-methodology}

\input{sections/4-experiments}




\section{Conclusion} \label{sec:conclusion}

In summary, we introduced cubical multiparameter persistence for dermoscopic image analysis and showed that topology provides complementary signal to modern vision backbones for skin cancer classification. Across multiple public datasets, multipersistence alone was competitive with strong pretrained baselines, and when aligned with a Vision Transformer via supervised contrastive learning it improved in-distribution performance over CNN and ViT models and, under cross-dataset transfer, achieved competitive AUC. Ablations confirmed that multiparameter topology yields richer cues than single-parameter cubical persistence and that contrastive alignment is important for effective fusion. Beyond performance, we provide concrete qualitative examples where the predicted class can be inspected together with the corresponding Betti curves and multipersistence maps, offering an interpretable auxiliary view of lesion structure that supports model auditing and error analysis. Limitations include the need to choose filtration parameters and grids, the computational overhead of multipersistence, and evaluation focused primarily on dermoscopy. Future work will explore adaptive and differentiable multiparameter filtrations, tighter end-to-end training with vision transformers.



\midlacknowledgments{This work was partially supported by National Science Foundation under grants DMS-2220613, and DMS-2229417. The authors acknowledge the \href{http://www.tacc.utexas.edu}{Texas Advanced Computing Center} (TACC) at  UT Austin for providing computational resources that have contributed to the research results reported within this paper.}


\bibliography{midl26_170}

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\appendix

\input{sections/5-appendix}

\end{document}