762 citations · 833 across the 28 of their papers we have counts for
17 papers · 1 filter
RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Pedro R. A. S. Bassi, Wenxuan Li, Hanxue Gu +11
Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, tumor segmentation masks are…
LightMedSeg-ISLES: Stroke Lesion Segmentation with 81x Fewer Parameters than nnU-Net
Giorgi Nikvashvili, Hanxue Gu, Jie Bao +2
Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present LightMedSeg-ISLES, a 1.26-m…
Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging
Bo Liu, Hanxue Gu, Xiangru Li +6
Evaluating vision-language models (VLMs) on medical images requires benchmarks that are clinically grounded, scalable, and controlled for evaluation confounds. Existing public benc…
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification
Yuwen Chen, Yaqian Chen, Roy Colglazier +4
Lower extremity computed tomography (CT) contains clinically relevant information for body composition analysis, sarcopenia assessment, and musculoskeletal disease monitoring, but…
Fully Automated Deep Learning Based Glenoid Bone Loss Measurement and Severity Stratification on 3D CT in Shoulder Instability
Zhonghao Liu, Hanxue Gu, Qihang Li +4
To develop and validate a fully automated, deep-learning pipeline for measuring glenoid bone loss on 3D CT scans using linear-based, en-face view, and best-circle method. Shoulder…
Transplant-Ready? Evaluating AI Lung Segmentation Models in Candidates with Severe Lung Disease
Jisoo Lee, Michael R. Harowicz, Yuwen Chen +5
This study evaluates publicly available deep-learning based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels,…
Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Hanxue Gu, Yaqian Chen, Jisoo Lee +5
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combi…
Breast density in MRI: an AI-based quantification and relationship to assessment in mammography
Yaqian Chen, Lin Li, Hanxue Gu +5
Mammographic breast density is a well-established risk factor for breast cancer. Recently there has been interest in breast MRI as an adjunct to mammography, as this modality provi…
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Nicholas Konz, Richard Osuala, Preeti Verma +16
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, t…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Segment anything model 2: an application to 2D and 3D medical images
Haoyu Dong, Hanxue Gu, Yaqian Chen +3
Segment Anything Model (SAM) has gained significant attention because of its ability to segment various objects in images given a prompt. The recently developed SAM 2 has extended…
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Hanxue Gu, Haoyu Dong, Jichen Yang +1
Automated segmentation is a fundamental medical image analysis task, which enjoys significant advances due to the advent of deep learning. While foundation models have been useful…
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…
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
Yixin Zhang, Shen Zhao, Hanxue Gu +1
Deep neural networks (DNNs) have demonstrated exceptional performance across various image segmentation tasks. However, the process of preparing datasets for training segmentation…
A systematic study of the foreground-background imbalance problem in deep learning for object detection
Hanxue Gu, Haoyu Dong, Nicholas Konz +1
The class imbalance problem in deep learning has been explored in several studies, but there has yet to be a systematic analysis of this phenomenon in object detection. Here, we pr…
Segment Anything Model for Medical Image Analysis: an Experimental Study
Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu +3
Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model…
Lightweight Transformer Backbone for Medical Object Detection
Yifan Zhang, Haoyu Dong, Nicholas Konz +2
Lesion detection in digital breast tomosynthesis (DBT) is an important and a challenging problem characterized by a low prevalence of images containing tumors. Due to the label sca…