9 citations · 28 across the 16 of their papers we have counts for
7 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,…
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…
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…
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…