4k citations · 5.5k across the 25 of their papers we have counts for
6 papers · 1 filter
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning
Ruxiao Duan, Jieneng Chen, Adam Kortylewski +2
Replay-based methods in class-incremental learning (CIL) have attained remarkable success. Despite their effectiveness, the inherent memory restriction results in saving a limited…
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
Jieneng Chen, Jieru Mei, Xianhang Li +12
Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, h…
Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation
Ju He, Jieneng Chen, Ming-Xian Lin +2
In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate object and part segmentation as an optimization problem and build a…
Label-Free Liver Tumor Segmentation
Qixin Hu, Yixiong Chen, Junfei Xiao +4
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intrig…
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Jieneng Chen, Yingda Xia, Jiawen Yao +22
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus o…
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen +7
An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem…