4 citations · 11 across the 7 of their papers we have counts for
7 papers
Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review
Mingze Yuan, Peng Bao, Jiajia Yuan +8
With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning.…
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
Hexin Dong, Jiawen Yao, Yuxing Tang +11
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients.…
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
Mingze Yuan, Yingda Xia, Xin Chen +10
Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, an…
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
Zifan Chen, Jiazheng Li, Jie Zhao +5
**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors,…
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization
Mingze Yuan, Yingda Xia, Hexin Dong +13
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically signi…
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Hexin Dong, Zifan Chen, Mingze Yuan +5
As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally…
Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
Hexin Dong, Fei Yu, Jie Zhao +2
This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, hel…