activity
20222024
most citedThe Intrinsic Manifolds of Radiological Images and their Role in Deep Learning

9 citations · 22 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL20242 cited

Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports

Mohamed Sobhi Jabal, Pranav Warman, Jikai Zhang +6

Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large langu…

eess.IV2024

SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images

Zafer Yildiz, Yuwen Chen, Maciej A. Mazurowski

Creating annotations for 3D medical data is time-consuming and often requires highly specialized expertise. Various tools have been implemented to aid this process. Segment Anythin…

eess.IV20241 cited

Rethinking Perceptual Metrics for Medical Image Translation

Nicholas Konz, Yuwen Chen, Hanxue Gu +2

Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI. Evaluating these methods typically relies on some chosen downst…

eess.IV20241 cited

Deep learning automates Cobb angle measurement compared with multi-expert observers

Keyu Li, Hanxue Gu, Roy Colglazier +11

Scoliosis, a prevalent condition characterized by abnormal spinal curvature leading to deformity, requires precise assessment methods for effective diagnosis and management. The Co…

cs.CV20242 cited

The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images

Nicholas Konz, Maciej A. Mazurowski

This paper investigates discrepancies in how neural networks learn from different imaging domains, which are commonly overlooked when adopting computer vision techniques from the d…

cs.CV2024

Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation

Haoyu Dong, Nicholas Konz, Hanxue Gu +1

Test-time adaptation (TTA) refers to adapting a trained model to a new domain during testing. Existing TTA techniques rely on having multiple test images from the same domain, yet…