309 citations · 331 across the 8 of their papers we have counts for
13 papers · 1 filter
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Hongwei Li, Fei-Fei Xue, Krishna Chaitanya +5
Radiomic representations can quantify properties of regions of interest in medical image data. Classically, they account for pre-defined statistics of shape, texture, and other low…
Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation
Jingkun Chen, Wenqi Li, Hongwei Li +1
Multi-modal medical image segmentation plays an essential role in clinical diagnosis. It remains challenging as the input modalities are often not well-aligned spatially. Existing…
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns
Hongwei Li, Timo Loehr, Anjany Sekuboyina +3
In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying…
Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks
Xiang Li, Jiechao Ma, Hongwei Li
Early diagnosis of pathological invasiveness of pulmonary adenocarcinomas using computed tomography (CT) imaging would alter the course of treatment of adenocarcinomas and subseque…
Cross-view Relation Networks for Mammogram Mass Detection
Jiechao Ma, Sen Liang, Xiang Li +4
Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage. The information from the two paired views (i.e., medio-lateral o…
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
Hongwei Li, Johannes C. Paetzold, Anjany Sekuboyina +5
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e.g. due to motion). Naturally, the idea ar…