38 citations · 88 across the 64 of their papers we have counts for
8 papers · 2 filters
Text-Driven Tumor Synthesis
Xinran Li, Yi Shuai, Chen Liu +11
Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases. However, existing synthesis methods, wh…
Analyzing Tumors by Synthesis
Qi Chen, Yuxiang Lai, Xiaoxi Chen +3
Computer-aided tumor detection has shown great potential in enhancing the interpretation of over 80 million CT scans performed annually in the United States. However, challenges ar…
Embracing Massive Medical Data
Yu-Cheng Chou, Zongwei Zhou, Alan Yuille
As massive medical data become available with an increasing number of scans, expanding classes, and varying sources, prevalent training paradigms -- where AI is trained with multip…
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
Jie Liu, Yixiao Zhang, Kang Wang +8
The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detail…
Towards Generalizable Tumor Synthesis
Qi Chen, Xiaoxi Chen, Haorui Song +4
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. However, success in tumor…
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
Tiange Xiang, Yixiao Zhang, Yongyi Lu +4
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploit…