86 citations · 397 across the 60 of their papers we have counts for
35 papers · 1 filter
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…
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,…
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…
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…
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…
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
Jinzheng Cai, Youbao Tang, Ke Yan +4
Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to th…
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
Ke Yan, Jinzheng Cai, Dakai Jin +7
Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying…
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…
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
Ke Yan, Jinzheng Cai, Youjing Zheng +7
Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often eit…
Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
Jinzheng Cai, Ke Yan, Chi-Tung Cheng +4
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/…
Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo +10
Determining the spread of GTV is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy fo…
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
Haomin Chen, Yirui Wang, Kang Zheng +8
Visual cues of enforcing bilaterally symmetric anatomies as normal findings are widely used in clinical practice to disambiguate subtle abnormalities from medical images. So far, i…
Learning to Segment Anatomical Structures Accurately from One Exemplar
Yuhang Lu, Weijian Li, Kang Zheng +8
Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotatio…
Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng +7
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and t…
Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
Ke Yan, Jinzheng Cai, Adam P. Harrison +3
Lesion detection is an important problem within medical imaging analysis. Most previous work focuses on detecting and segmenting a specialized category of lesions (e.g., lung nodul…
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
Zhuotun Zhu, Ke Yan, Dakai Jin +9
Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of on…
Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search
Dazhou Guo, Dakai Jin, Zhuotun Zhu +7
OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated…
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Weijian Li, Yuhang Lu, Kang Zheng +8
Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to cap…
Weakly Supervised Lesion Co-segmentation on CT Scans
Vatsal Agarwal, Youbao Tang, Jing Xiao +1
Lesion segmentation in medical imaging serves as an effective tool for assessing tumor sizes and monitoring changes in growth. However, not only is manual lesion segmentation time-…