6 papers · 1 filter
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…
AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking
Wenxuan Li, Chongyu Qu, Xiaoxi Chen +18
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and f…
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…
From Pixel to Cancer: Cellular Automata in Computed Tomography
Yuxiang Lai, Xiaoxi Chen, Angtian Wang +2
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in…
Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?
Tiezheng Zhang, Xiaoxi Chen, Chongyu Qu +2
Interactive segmentation, an integration of AI algorithms and human expertise, premises to improve the accuracy and efficiency of curating large-scale, detailed-annotated datasets…