189 citations · 213 across the 14 of their papers we have counts for
6 papers · 1 filter
Compound Figure Separation of Biomedical Images with Side Loss
Tianyuan Yao, Chang Qu, Quan Liu +11
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large…
VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning
Mengyang Zhao, Quan Liu, Aadarsh Jha +6
Recent advances in bioimaging have provided scientists a superior high spatial-temporal resolution to observe dynamics of living cells as 3D volumetric videos. Unfortunately, the 3…
SimTriplet: Simple Triplet Representation Learning with a Single GPU
Quan Liu, Peter C. Louis, Yuzhe Lu +9
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…
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis
Yuzhe Lu, Aadarsh Jha, Yuankai Huo
Annotated medical images are typically rarer than labeled natural images since they are limited by domain knowledge and privacy constraints. Recent advances in transfer and contras…
BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation
Xing Li, Haichun Yang, Jiaxin He +5
Reducing outcome variance is an essential task in deep learning based medical image analysis. Bootstrap aggregating, also known as bagging, is a canonical ensemble algorithm for ag…
ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations
Quan Liu, Isabella M. Gaeta, Mengyang Zhao +6
Background: The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method consists of…