4 papers
Rewarding Reasoning, Not Answers: Fixing and Bounding Test-Time Reinforcement Learning on Medical QA
Kailong Fan, Anqi Pu, Yichen Wu +7
Test-time reinforcement learning adapts a model on its own unlabeled test set using majority-vote pseudo-labels and has shown strong results in mathematics. We show that this recip…
MAPLE: Elevating Medical Reasoning from Statistical Consensus to Process-Led Alignment
Kailong Fan, Anqi Pu, Yichen Wu +7
Recent advances in medical large language models have explored Test-Time Reinforcement Learning (TTRL) to enhance reasoning. However, standard TTRL often relies on majority voting…
OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging
Sifan Song, Siyeop Yoon, Pengfei Jin +10
Recent advances in representation learning often rely on holistic embeddings that entangle multiple semantic components, limiting interpretability and generalization. These issues…
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts
Runqi Meng, Sifan Song, Pengfei Jin +9
Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with:…