1 citations · 1 across the 16 of their papers we have counts for
17 papers
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
Sara Rezaeimanesh, Chad Klochko, Kundan Thind +1
Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed. In practice, however, data a…
A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
Rafi Ibn Sultan, Chengyin Li, Yiannos Demetriou +6
Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.…
Vision-Language Model Confidence Is Not a Property of the Answer
Reza Khanmohammadi, Erfan Miahi, Ivan Brugere +4
Vision-language models are increasingly deployed behind a confidence gate: the system reads how confident the model is in its answer and defers when confidence is low. This makes t…
Adapt or Calibrate? How Local Labels Buy Certified Coverage in Medical VQA
Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
An answer from a vision-language model (VLM) to a medical image question can be accepted without review only under a threshold bounding the error of accepted answers, and certifyin…
Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
Large vision-language models (LVLMs) suffer from visual ungroundedness: they can produce a fluent, confident, and even correct response driven entirely by language priors, with the…
Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations
Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim +2
Learning-based fluence map prediction offers a fast alternative to iterative inverse planning in intensity-modulated radiation therapy (IMRT), but its robustness under realistic di…
How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
The miscalibration of Large Reasoning Models (LRMs) undermines their reliability in high-stakes domains, necessitating methods to accurately estimate the confidence of their long-f…
What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization
Sara Rezaeimanesh, Kundan Thind, Farzan Siddiqui +1
In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that struggle to generalize to real-worl…
Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent
Humza Nusrat, Luke Francisco, Bing Luo +10
Stereotactic radiosurgery (SRS) demands precise dose shaping around critical structures, yet black-box AI systems have limited clinical adoption due to opacity concerns. We tested…
FluenceFormer: Transformer-Driven Multi-Beam Fluence Map Regression for Radiotherapy Planning
Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim +2
Fluence map prediction is central to automated radiotherapy planning but remains an ill-posed inverse problem due to the complex relationship between volumetric anatomy and beam-in…
Calibrating LLM Confidence by Probing Perturbed Representation Stability
Reza Khanmohammadi, Erfan Miahi, Mehrsa Mardikoraem +5
Miscalibration in Large Language Models (LLMs) undermines their reliability, highlighting the need for accurate confidence estimation. We introduce CCPS (Calibrating LLM Confidence…
Characterization and ex vivo application of flexible 2D scintillating coatings in ultra-high dose rate electron beams for FLASH radiotherapy
Verdi Vanreusel, Stephen Brown, Shujat Ali +11
The increasing interest in FLASH-RT has lead to the conversion of linear accelerators to enable ultra-high dose rate (UHDR) beams for preclinical research. Dosimetry hereof remains…
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent
Humza Nusrat, Bing Luo, Ryan Hall +5
Radiotherapy treatment planning is a complex and time-intensive process, often impacted by inter-planner variability and subjective decision-making. To address these challenges, we…
MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training
Chengyin Li, Hui Zhu, Rafi Ibn Sultan +5
In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomog…
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction
Reza Khanmohammadi, Ahmed I. Ghanem, Kyle Verdecchia +9
Large Language Models (LLMs) offer significant potential for clinical symptom extraction, but their deployment in healthcare settings is constrained by privacy concerns, computatio…
Iterative Prompt Refinement for Radiation Oncology Symptom Extraction Using Teacher-Student Large Language Models
Reza Khanmohammadi, Ahmed I Ghanem, Kyle Verdecchia +7
This study introduces a novel teacher-student architecture utilizing Large Language Models (LLMs) to improve prostate cancer radiotherapy symptom extraction from clinical notes. Mi…
An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology
Reza Khanmohammadi, Mohammad M. Ghassemi, Kyle Verdecchia +8
Natural Language Processing (NLP) is a key technique for developing Medical Artificial Intelligence (AI) systems that leverage Electronic Health Record (EHR) data to build diagnost…