239 citations · 963 across the 124 of their papers we have counts for
17 papers · 1 filter
Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data
Ruining Deng, Quan Liu, Can Cui +3
Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer patho…
Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes
Jiachen Xu, Junlin Guo, James Zimmer-Dauphinee +7
Archaeology has long faced fundamental issues of sampling and scalar representation. Traditionally, the local-to-regional-scale views of settlement patterns are produced through sy…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Circle Representation for Medical Object Detection
Ethan H. Nguyen, Haichun Yang, Ruining Deng +7
Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g.,…
Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation
Cam Nguyen, Zuhayr Asad, Yuankai Huo
Histopathology has played an essential role in cancer diagnosis. With the rapid advances in convolutional neural networks (CNN). Various CNN-based automated pathological image segm…
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging
Shunxing Bao, Yucheng Tang, Ho Hin Lee +8
Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing…
Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT
Riqiang Gao, Mirza S. Khan, Yucheng Tang +6
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning. Potential data quality issues can be exacerbated when human-…
Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
Riqiang Gao, Yucheng Tang, Kaiwen Xu +7
Data from multi-modality provide complementary information in clinical prediction, but missing data in clinical cohorts limits the number of subjects in multi-modal learning contex…
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…
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
Ho Hin Lee, Yucheng Tang, Qi Yang +6
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask. To increase the ability of encoder-…
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…
Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix Augmentation
Yuzhe Lu, Haichun Yang, Zheyu Zhu +3
The classification of glomerular lesions is a routine and essential task in renal pathology. Recently, machine learning approaches, especially deep learning algorithms, have been u…
WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19
Zekun Wang, Pengwei Wang, Peter C. Louis +2
The COVID-19 epidemic has been a significant healthcare challenge in the United States. According to the Centers for Disease Control and Prevention (CDC), COVID-19 infection is tra…
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…