86 citations · 397 across the 60 of their papers we have counts for
62 papers
A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging
Zhilin Zheng, Xu Fang, Jiawen Yao +10
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in gene…
Localized Adversarial Domain Generalization
Wei Zhu, Le Lu, Jing Xiao +3
Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attent…
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
Fakai Wang, Kang Zheng, Le Lu +4
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-…
Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis
Kang Zheng, Yirui Wang, Chen-I Hsieh +4
Knee osteoarthritis (OA) is a common degenerate joint disorder that affects a large population of elderly people worldwide. Accurate radiographic assessment of knee OA severity pla…
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
Dazhou Guo, Jia Ge, Xianghua Ye +22
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head…
A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI
Peng Wang, Yuhsuan Wu, Bolin Lai +9
Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients w…
Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning
Bowen Li, Dar-In Tai, Ke Yan +7
Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated…
Multi-institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume using planning-CT and FDG-PETCT
Xianghua Ye, Dazhou Guo, Chen-kan Tseng +22
Background: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation of high labor-costs and interuser variability. Purpose: To…
SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings
Fengze Liu, Ke Yan, Adam Harrison +8
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of compu…
DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search
Dazhou Guo, Xianghua Ye, Jia Ge +9
Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists a…
Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection
Youbao Tang, Ke Yan, Jinzheng Cai +6
Measuring lesion size is an important step to assess tumor growth and monitor disease progression and therapy response in oncology image analysis. Although it is tedious and highly…
Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss
Youbao Tang, Jinzheng Cai, Ke Yan +6
Accurately segmenting a variety of clinically significant lesions from whole body computed tomography (CT) scans is a critical task on precision oncology imaging, denoted as univer…
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
Xiao-Yun Zhou, Bolin Lai, Weijian Li +12
Landmark localization plays an important role in medical image analysis. Learning based methods, including CNN and GCN, have demonstrated the state-of-the-art performance. However,…
Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray
Fakai Wang, Kang Zheng, Yirui Wang +6
Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, Dual-e…
Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings
Bowen Li, Xinping Ren, Ke Yan +6
Depending on the application, radiological diagnoses can be associated with high inter- and intra-rater variabilities. Most computer-aided diagnosis (CAD) solutions treat such data…
Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data
Bolin Lai, Yuhsuan Wu, Xiao-Yun Zhou +7
Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the hi…
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
Jieneng Chen, Ke Yan, Yu-Dong Zhang +9
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tu…
Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images
Kang Zheng, Yirui Wang, Xiaoyun Zhou +8
Bone mineral density (BMD) is a clinically critical indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Due to the limited accessibility of DEXA…
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Xinyu Zhang, Yirui Wang, Chi-Tung Cheng +5
Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxe…
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
Fakai Wang, Kang Zheng, Le Lu +3
Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are p…
3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management
Tianyi Zhao, Kai Cao, Jiawen Yao +6
The pancreatic disease taxonomy includes ten types of masses (tumors or cysts)[20,8]. Previous work focuses on developing segmentation or classification methods only for certain ma…
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
Yuhang Lu, Kang Zheng, Weijian Li +8
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gather…
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays
Yirui Wang, Kang Zheng, Chi-Tung Chang +7
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohib…
Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach
Bolin Lai, Yuhsuan Wu, Xiaoyu Bai +10
Using radiological scans to identify liver tumors is crucial for proper patient treatment. This is highly challenging, as top radiologists only achieve F1 scores of roughly 80% (he…