76 citations · 335 across the 47 of their papers we have counts for
19 papers · 1 filter
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…
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…
Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
Haomin Chen, Shun Miao, Daguang Xu +2
CXRs are a crucial and extraordinarily common diagnostic tool, leading to heavy research for CAD solutions. However, both high classification accuracy and meaningful model predicti…
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…
Reliable Liver Fibrosis Assessment from Ultrasound using Global Hetero-Image Fusion and View-Specific Parameterization
Bowen Li, Ke Yan, Dar-In Tai +4
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep…
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…
Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation
Ashwin Raju, Chi-Tung Cheng, Yunakai Huo +6
In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalitie…
JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
Fengze Liu, Jinzheng Cai, Yuankai Huo +8
Multi-modal image registration is a challenging problem that is also an important clinical task for many real applications and scenarios. As a first step in analysis, deformable re…
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…
Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale
Jinzheng Cai, Adam P. Harrison, Youjing Zheng +5
Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent d…