16 citations · 16 across the 5 of their papers we have counts for
5 papers
MARIO: A Mixed Annotation Framework For Polyp Segmentation
Haoyang Li, Yiwen Hu, Jun Wei +1
Existing polyp segmentation models are limited by high labeling costs and the small size of datasets. Additionally, vast polyp datasets remain underutilized because these models ty…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
MixPolyp: Integrating Mask, Box and Scribble Supervision for Enhanced Polyp Segmentation
Yiwen Hu, Jun Wei, Yuncheng Jiang +4
Limited by the expensive labeling, polyp segmentation models are plagued by data shortages. To tackle this, we propose the mixed supervised polyp segmentation paradigm (MixPolyp).…
Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development
Yuncheng Jiang, Yiwen Hu, Zixun Zhang +7
Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the la…
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
Yuncheng Jiang, Zixun Zhang, Yiwen Hu +6
Accurate polyp detection is critical for early colorectal cancer diagnosis. Although remarkable progress has been achieved in recent years, the complex colon environment and concea…