3 citations · 3 across the 14 of their papers we have counts for
15 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…
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…
Benchmarking LLM Summaries of Multimodal Clinical Time Series for Remote Monitoring
Aditya Shukla, Yining Yuan, Ben Tamo +7
Large language models (LLMs) can generate fluent clinical summaries of remote therapeutic monitoring time series. However, it remains unclear whether these narratives faithfully ca…
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…
Causal Machine Learning for Surgical Interventions
J. Ben Tamo, Nishant S. Chouhan, Micky C. Nnamdi +6
Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spina…
MENDR: Manifold Explainable Neural Data Representations
Matthew Chen, Micky Nnamdi, Justin Shao +7
Foundation models for electroencephalography (EEG) signals have recently demonstrated success in learning generalized representations of EEGs, outperforming specialized models in v…
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…
Advancing Problem-Based Learning in Biomedical Engineering in the Era of Generative AI
Micky C. Nnamdi, J. Ben Tamo, Benoit Marteau +2
Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking an…
Monitoring Health Using IoT and Thingspeak
Micky Nnamdi, Peter Joboson, Charles Bala +1
Quality of health is greatly impacted by the quality of measurement, hence active monitoring of patient's vitals status greatly affects recovery and improves prognosis. In this pap…
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…