762 citations · 833 across the 28 of their papers we have counts for
4 papers · 1 filter
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…
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs
Hanxue Gu, Hongyu He, Roy Colglazier +3
Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and ty…