Vishwesh Ramanathan

Jianan Chen, Vishwesh Ramanathan, Tony Xu et al.

Machine learning models experience deteriorated performance when trained in the presence of noisy labels. This is particularly problematic for medical tasks, such as survival prediction, which typically face high label noise complexity with few clear-cut solutions. Inspired by the large fluctuations across folds in the cross-validation performance of survival analyses, we design Monte-Carlo experiments to show that such fluctuation could be caused by label noise. We propose two novel and straightforward label noise detection algorithms that effectively identify noisy examples by pinpointing the samples that more frequently contribute to inferior cross-validation results. We first introduce Repeated Cross-Validation (ReCoV), a parameter-free label noise detection algorithm that is robust to model choice. We further develop fastReCoV, a less robust but more tractable and efficient variant of ReCoV suitable for deep learning applications. Through extensive experiments, we show that ReCoV and fastReCoV achieve state-of-the-art label noise detection performance in a wide range of modalities, models and tasks, including survival analysis, which has yet to be addressed in the literature. Our code and data are publicly available at https://github.com/GJiananChen/ReCoV.

Vishwesh Ramanathan, Tony Xu, Pushpak Pati et al.

Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. Advances in computing, data availability, and self-supervised learning (SSL) have paved the way for slide-level foundation models (SLFMs) that can improve prediction tasks in low-data regimes. However, current methods under-utilize shared information between tasks and modalities. To overcome this challenge, we propose ModalTune, a novel fine-tuning framework which introduces the Modal Adapter to integrate new modalities without modifying SLFM weights. Additionally, we use large-language models (LLMs) to encode labels as text, capturing semantic relationships across multiple tasks and cancer types in a single training recipe. ModalTune achieves state-of-the-art (SOTA) results against both uni-modal and multi-modal models across four cancer types, jointly improving survival and cancer subtype prediction while remaining competitive in pan-cancer settings. Additionally, we show ModalTune is generalizable to two out-of-distribution (OOD) datasets. To our knowledge, this is the first unified fine-tuning framework for multi-modal, multi-task, and pan-cancer modeling in digital pathology.