13 papers
Beyond Random Splits: Evaluating Drug-Target Affinity Models Under Chemically and Biologically Motivated Distribution Shifts Copy
Minjae Chung, Clara Li, Malar Paavai Muthukumaran +8
Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compared under a single data split…
ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents
Yuxing Lu, Yushuhong Lin, Wenqi Shi +4
Clinical practice is not the selection of an answer from enumerated options: a physician gathers heterogeneous information incrementally and commits to sequential, irreversible dec…
Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding
Micky C. Nnamdi, Benoit L. Marteau, Yishan Zhong +2
Large Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Current interpretability methods, su…
RobustMedSAM: Degradation-Resilient Medical Image Segmentation via Robust Foundation Model Adaptation
Jieru Li, Matthew Chen, Micky C. Nnamdi +3
Medical image segmentation models built on Segment Anything Model (SAM) achieve strong performance on clean benchmarks, yet their reliability often degrades under realistic image c…
KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry
Micky C Nnamdi, Wenqi Shi, Cheng Wan +4
Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through poly…
EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Models
J. Ben Tamo, Yuxing Lu, Benoit L. Marteau +2
Large Language Models (LLMs) are fluent but prone to hallucinations, producing answers that appear plausible yet are unsupported by available evidence. This failure is especially p…
LLM-as-RNN: A Recurrent Language Model for Memory Updates and Sequence Prediction
Yuxing Lu, J. Ben Tamo, Weichen Zhao +5
Large language models are strong sequence predictors, yet standard inference relies on immutable context histories. After making an error at generation step t, the model lacks an u…
LinguaMap: Which Layers of LLMs Speak Your Language and How to Tune Them?
J. Ben Tamo, Daniel Carlander-Reuterfelt, Jonathan Rubin +3
Despite multilingual pretraining, large language models often struggle with non-English tasks, particularly in language control, the ability to respond in the intended language. We…
Developing Fairness-Aware Task Decomposition to Improve Equity in Post-Spinal Fusion Complication Prediction
Yining Yuan, J. Ben Tamo, Wenqi Shi +5
Fairness in clinical prediction models remains a persistent challenge, particularly in high-stakes applications such as spinal fusion surgery for scoliosis, where patient outcomes…
Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosis
Yining Yuan, J. Ben Tamo, Micky C. Nnamdi +2
Large language models (LLMs) show promise in automating clinical diagnosis, yet their non-transparent decision-making and limited alignment with diagnostic standards hinder trust a…
MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics
Yuxing Lu, Xukai Zhao, J. Ben Tamo +6
Large Language Models (LLMs) have demonstrated remarkable capabilities on general text; however, their proficiency in specialized scientific domains that require deep, interconnect…
Novel Extraction of Discriminative Fine-Grained Feature to Improve Retinal Vessel Segmentation
Shuang Zeng, Chee Hong Lee, Micky C Nnamdi +9
Retinal vessel segmentation is a vital early detection method for several severe ocular diseases. Despite significant progress in retinal vessel segmentation with the advancement o…
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-training
Shuang Zeng, Lei Zhu, Xinliang Zhang +10
Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learni…