Masked Autoencoder Joint Learning for Robust Spitzoid Tumor Classification
This work addresses the challenge of accurate diagnosis for spitzoid tumors, a critical medical issue, by handling incomplete methylation data, though it appears incremental as an extension of an existing method.
The paper tackled the problem of classifying spitzoid tumors using DNA methylation data, which often has missing entries, by introducing ReMAC, an extension of ReMasker, and demonstrated that it achieves strong and robust performance compared to other methods on real clinical data.
Accurate diagnosis of spitzoid tumors (ST) is critical to ensure a favorable prognosis and to avoid both under- and over-treatment. Epigenetic data, particularly DNA methylation, provide a valuable source of information for this task. However, prior studies assume complete data, an unrealistic setting as methylation profiles frequently contain missing entries due to limited coverage and experimental artifacts. Our work challenges these favorable scenarios and introduces ReMAC, an extension of ReMasker designed to tackle classification tasks on high-dimensional data under complete and incomplete regimes. Evaluation on real clinical data demonstrates that ReMAC achieves strong and robust performance compared to competing classification methods in the stratification of ST. Code is available: https://github.com/roshni-mahtani/ReMAC.