We present an approach for compressing volumetric scalar fields using implicit neural
representations. Our approach represents a scalar field as a learned function, wherein a neural …

Writing radiology reports from medical images requires a high level of domain expertise. It is
time-consuming even for trained radiologists and can be error-prone for inexperienced …

Out-of-distribution (OOD) data poses serious challenges in deployed machine learning
models, so methods of predicting a model's performance on OOD data without labels are …

Box representation has been extensively used for object detection in computer vision. Such
representation is efficacious but not necessarily optimized for biomedical objects (eg, …

Measuring similarities between different tasks is critical in a broad spectrum of machine
learning problems, including transfer, multi-task, continual, and meta-learning. Most current …

Object detection networks are powerful in computer vision, but not necessarily optimized for
biomedical object detection. In this work, we propose CircleNet, a simple anchor-free …

Out-of-distribution (OOD) data poses serious challenges in deployed machine learning
models as even subtle changes could incur significant performance drops. Being able to …

Contrastive learning is a key technique of modern self-supervised learning. The broader
accessibility of earlier approaches is hindered by the need of heavy computational resources (…

Purpose: The quantitative detection, segmentation, and characterization of glomeruli from
high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted …

In this paper we present a reconstruction technique for the reduction of unsteady flow data
based on neural representations of time‐varying vector fields. Our approach is motivated by …