35 citations · 51 across the 11 of their papers we have counts for
11 papers
Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling
Zhemin Zhang, Weijie Chen, David Le +7
Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift. We introduce a foundation model-driven f…
From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare
Man Luo, Bahareh Harandizadeh, Amara Tariq +5
Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of cli…
Position: Restructuring of Categories and Implementation of Guidelines Essential for VLM Adoption in Healthcare
Amara Tariq, Rimita Lahiri, Charles Kahn +1
The intricate and multifaceted nature of vision language model (VLM) development, adaptation, and application necessitates the establishment of clear and standardized reporting pro…
Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports
David Le, Ramon Correa-Medero, Amara Tariq +3
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and pro…
Knowledge-grounded Adaptation Strategy for Vision-language Models: Building Unique Case-set for Screening Mammograms for Residents Training
Aisha Urooj Khan, John Garrett, Tyler Bradshaw +4
A visual-language model (VLM) pre-trained on natural images and text pairs poses a significant barrier when applied to medical contexts due to domain shift. Yet, adapting or fine-t…
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain
Thiago Santos, Amara Tariq, Susmita Das +4
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pat…
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Siyi Tang, Amara Tariq, Jared Dunnmon +5
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk…
Generalization of Deep Convolutional Neural Networks -- A Case-study on Open-source Chest Radiographs
Nazanin Mashhaditafreshi, Amara Tariq, Judy Wawira Gichoya +1
Deep Convolutional Neural Networks (DCNNs) have attracted extensive attention and been applied in many areas, including medical image analysis and clinical diagnosis. One major cha…
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
Imon Banerjee, Priyanshu Sinha, Saptarshi Purkayastha +5
Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the…
Learning Semantics for Image Annotation
Amara Tariq, Hassan Foroosh
Image search and retrieval engines rely heavily on textual annotation in order to match word queries to a set of candidate images. A system that can automatically annotate images w…
Image Annotation using Multi-Layer Sparse Coding
Amara Tariq, Hassan Foroosh
Automatic annotation of images with descriptive words is a challenging problem with vast applications in the areas of image search and retrieval. This problem can be viewed as a la…