Towards generalizable tumor synthesis

Q Chen, X Chen, H Song, Z **ong… - Proceedings of the …, 2024‏ - openaccess.thecvf.com
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 …

Abdomenatlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking

W Li, C Qu, X Chen, PRAS Bassi, Y Shi, Y Lai… - Medical Image …, 2024‏ - Elsevier
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-
dimensional CT volumes sourced from 112 hospitals across diverse populations …

Abdomenatlas-8k: Annotating 8,000 ct volumes for multi-organ segmentation in three weeks

C Qu, T Zhang, H Qiao, Y Tang… - Advances in Neural …, 2023‏ - proceedings.neurips.cc
Annotating medical images, particularly for organ segmentation, is laborious and time-
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …

Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography

J Liu, Y Zhang, K Wang, MC Yavuz, X Chen… - Medical image …, 2024‏ - Elsevier
The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is
propelled by the growing availability of computed tomography (CT) datasets with detailed …

[HTML][HTML] Medshapenet–a large-scale dataset of 3d medical shapes for computer vision

J Li, Z Zhou, J Yang, A Pepe, C Gsaxner… - Biomedical …, 2025‏ - degruyter.com
Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms
in medical imaging are predominantly diverging from computer vision, where voxel grids …

Touchstone benchmark: Are we on the right way for evaluating AI algorithms for medical segmentation?

PRAS Bassi, W Li, Y Tang, F Isensee… - Advances in …, 2025‏ - proceedings.neurips.cc
How can we test AI performance? This question seems trivial, but it isn't. Standard
benchmarks often have problems such as in-distribution and small-size test sets …

From pixel to cancer: Cellular automata in computed tomography

Y Lai, X Chen, A Wang, A Yuille, Z Zhou - International Conference on …, 2024‏ - Springer
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 medical …

Exploiting structural consistency of chest anatomy for unsupervised anomaly detection in radiography images

T **ang, Y Zhang, Y Lu, A Yuille… - … on Pattern Analysis …, 2024‏ - ieeexplore.ieee.org
Radiography imaging protocols focus on particular body regions, therefore producing
images of great similarity and yielding recurrent anatomical structures across patients …

Boosting dermatoscopic lesion segmentation via diffusion models with visual and textual prompts

S Du, X Wang, Y Lu, Y Zhou, S Zhang… - 2024 IEEE …, 2024‏ - ieeexplore.ieee.org
Image synthesis approaches, eg, generative adversarial networks, have been popular as a
form of data augmentation in medical image analysis tasks. It is primarily beneficial to …

Acquiring weak annotations for tumor localization in temporal and volumetric data

YC Chou, B Li, DP Fan, A Yuille, Z Zhou - Machine Intelligence Research, 2024‏ - Springer
Creating large-scale and well-annotated datasets to train AI algorithms is crucial for
automated tumor detection and localization. However, with limited resources, it is …