7 citations · 8 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…