762 citations · 833 across the 25 of their papers we have counts for
27 papers
Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
Joakim Nguyen, Jian Yu, Jinrui Fang +7
Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-…
PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification
Jian Yu, Joakim Nguyen, Jinrui Fang +10
Accurate classification of pediatric central nervous system tumors remains challenging due to histological complexity and limited training data. While pathology foundation models h…
Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz +2
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. Howeve…
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…
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
Yixin Zhang, Nicholas Konz, Kevin Kramer +1
Image segmentation foundation models (SFMs) like Segment Anything Model (SAM) have achieved impressive zero-shot and interactive segmentation across diverse domains. However, they…
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…
The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study
Brian Guo, Darui Lu, Gregory Szumel +4
Purpose: Medical images acquired using different scanners and protocols can differ substantially in their appearance. This phenomenon, scanner domain shift, can result in a drop in…
Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension
Nicholas Konz, Maciej A. Mazurowski
In recent years, there has been interest in how geometric properties such as intrinsic dimension (ID) of a neural network's hidden representations change through its layers, and ho…
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…
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…
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…
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…
Understanding the Inner Workings of Language Models Through Representation Dissimilarity
Davis Brown, Charles Godfrey, Nicholas Konz +2
As language models are applied to an increasing number of real-world applications, understanding their inner workings has become an important issue in model trust, interpretability…
Attributing Learned Concepts in Neural Networks to Training Data
Nicholas Konz, Charles Godfrey, Madelyn Shapiro +3
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data. As having the right (or…
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…
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
Nicholas Konz, Maciej A. Mazurowski
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain p…
Robust Chauvenet Rejection: Powerful, but Easy to Use Outlier Detection for Heavily Contaminated Data Sets
Nicholas Konz, Daniel E. Reichart
In Maples et al. (2018) we introduced Robust Chauvenet Outlier Rejection, or RCR, a novel outlier rejection technique that evolves Chauvenet's Criterion by sequentially applying di…
Deep Learning for Breast MRI Style Transfer with Limited Training Data
Shixing Cao, Nicholas Konz, James Duncan +1
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is…