762 citations · 845 across the 26 of their papers we have counts for
26 papers
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…
Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
Yuheng Li, Yuan Gao, Haoyu Dong +5
Computed tomography (CT) is a central to three-dimensional medical imaging, yet CT-based artificial intelligence remains fragmented across task-specific models for segmentation, cl…
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu +4
The widespread use of Magnetic Resonance Imaging (MRI) in combination with deep learning shows promise for many high-impact automated diagnostic and prognostic tools. However, trai…
SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
Roy Colglazier, Jisoo Lee, Haoyu Dong +12
The quantity and quality of muscles are increasingly recognized as important predictors of health outcomes. While MRI offers a valuable modality for such assessments, obtaining pre…
GuidedMorph: Two-Stage Deformable Registration for Breast MRI
Yaqian Chen, Hanxue Gu, Haoyu Dong +5
Accurately registering breast MR images from different time points enables the alignment of anatomical structures and tracking of tumor progression, supporting more effective breas…
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
Yuwen Chen, Zafer Yildiz, Qihang Li +5
Manual annotation of volumetric medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT), is a labor-intensive and time-consuming process. Recent advan…
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…
Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis
Yaqian Chen, Hanxue Gu, Yuwen Chen +7
Body composition assessment using CT images can potentially be used for a number of clinical applications, including the prognostication of cardiovascular outcomes, evaluation of m…
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…
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…
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…
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Yuwen Chen, Nicholas Konz, Hanxue Gu +5
Accurately translating medical images between different modalities, such as Computed Tomography (CT) to Magnetic Resonance Imaging (MRI), has numerous downstream clinical and machi…
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…
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Nicholas Konz, Yuwen Chen, Haoyu Dong +1
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images. To this end, we propose…
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
Hanxue Gu, Roy Colglazier, Haoyu Dong +20
Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of MRIs into different organs an…
Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph
Yixin Wang, Zihao Lin, Haoyu Dong
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation proces…
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…
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion
Nicholas Konz, Haoyu Dong, Maciej A. Mazurowski
Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity…
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
Nicholas Konz, Hanxue Gu, Haoyu Dong +1
The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural n…
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…
Trust It or Not: Confidence-Guided Automatic Radiology Report Generation
Yixin Wang, Zihao Lin, Zhe Xu +7
Medical imaging plays a pivotal role in diagnosis and treatment in clinical practice. Inspired by the significant progress in automatic image captioning, various deep learning (DL)…