7 citations · 11 across the 11 of their papers we have counts for
11 papers
Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation
Vince Zhu, Zhanghexuan Ji, Dazhou Guo +6
Deep segmentation networks achieve high performance when trained on specific datasets. However, in clinical practice, it is often desirable that pretrained segmentation models can…
Continual Domain Adversarial Adaptation via Double-Head Discriminators
Yan Shen, Zhanghexuan Ji, Chunwei Ma +1
Domain adversarial adaptation in a continual setting poses a significant challenge due to the limitations on accessing previous source domain data. Despite extensive research in co…
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Zhanghexuan Ji, Dazhou Guo, Puyang Wang +9
Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting…
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning
Chunwei Ma, Zhanghexuan Ji, Ziyun Huang +3
Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep…
A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning
Yan Shen, Zhanghexuan Ji, Chunwei Ma +1
Object tracking is one of the fundamental problems in visual recognition tasks and has achieved significant improvements in recent years. The achievements often come with the price…
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Yan Shen, Jian Du, Han Zhao +3
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of…
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber +3
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision…
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation
Yan Shen, Zhanghexuan Ji, Mingchen Gao
Many segmentation tasks for biomedical images can be modeled as the minimization of an energy function and solved by a class of max-flow and min-cut optimization algorithms. Howeve…
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
Ashwin Raju, Zhanghexuan Ji, Chi Tung Cheng +6
Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guid…
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation
Zhanghexuan Ji, Yan Shen, Chunwei Ma +1
The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually label…
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
Chunwei Ma, Zhanghexuan Ji, Mingchen Gao
Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment. Recent year…