9 citations · 16 across the 21 of their papers we have counts for
21 papers
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…
Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding
Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley +2
LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritative-looking answer is sometime…
Calibrated Triage, Not Autonomy: Confidence Estimation for Medical Vision-Language Models
Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language prior…
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…
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…
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…
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation
Niloufar Eghbali, Hassan Bagher-Ebadian, Tuka Alhanai +1
Vision Transformers (ViTs) have shown promise in medical image semantic segmentation (MISS) by capturing long-range correlations. However, ViTs often struggle to model local spatia…
Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework
Niloufar Eghbali, Tuka Alhanai, Mohammad M. Ghassemi
Mechanical Ventilation (MV) is a critical life-support intervention in intensive care units (ICUs). However, optimal ventilator settings are challenging to determine because of the…
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…
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals
Chuheng Wu, S. Farokh Atashzar, Mohammad M. Ghassemi +1
Surface Electromyography (sEMG) is a non-invasive signal that is used in the recognition of hand movement patterns, the diagnosis of diseases, and the robust control of prostheses.…
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…
Temporal Link Prediction Using Graph Embedding Dynamics
Sanaz Hasanzadeh Fard, Mohammad Ghassemi
Graphs are a powerful representation tool in machine learning applications, with link prediction being a key task in graph learning. Temporal link prediction in dynamic networks is…
The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains
Reza Khanmohammadi, Tuka Alhanai, Mohammad M. Ghassemi
Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to…
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks
Shangyang Min, Hassan B. Ebadian, Tuka Alhanai +1
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e.g. Entropy), and then embedded into other networks t…
MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs
Reza Khanmohammadi, Sari Saba-Sadiya, Sina Esfandiarpour +2
In this paper, we present Mambanet: a hybrid neural network for predicting the outcomes of Basketball games. Contrary to other studies, which focus primarily on season games, this…
Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach
Allen R. Williams, Yoolim Jin, Anthony Duer +2
In recent years it has become possible to collect GPS data from drivers and to incorporate this data into automobile insurance pricing for the driver. This data is continuously col…
Feature Imitating Networks
Sari Saba-Sadiya, Tuka Alhanai, Mohammad M Ghassemi
In this paper, we introduce a novel approach to neural learning: the Feature-Imitating-Network (FIN). A FIN is a neural network with weights that are initialized to reliably approx…
Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals
Reza Khanmohammadi, Mitra Sadat Mirshafiee, Mohammad Mahdi Ghassemi +1
Phonocardiogram (PCG) signal analysis is a critical, widely-studied technology to noninvasively analyze the heart's mechanical activity. Through evaluating heart sounds, this techn…
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations
Hooman Sedghamiz, Shivam Raval, Enrico Santus +2
While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised al…
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media
Shivam Raval, Hooman Sedghamiz, Enrico Santus +3
Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes i…
EEG Channel Interpolation Using Deep Encoder-decoder Netwoks
Sari Saba-Sadiya, Tuka Alhanai, Taosheng Liu +1
Electrode "pop" artifacts originate from the spontaneous loss of connectivity between a surface and an electrode. Electroencephalography (EEG) uses a dense array of electrodes, hen…