2.1k citations · 4k across the 21 of their papers we have counts for
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Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis
Bowen Li, Yu-Cheng Chou, Shuwen Sun +3
Early detection and localization of pancreatic cancer can increase the 5-year survival rate for patients from 8.5% to 20%. Artificial intelligence (AI) can potentially assist radio…
Continual Learning for Abdominal Multi-Organ and Tumor Segmentation
Yixiao Zhang, Xinyi Li, Huimiao Chen +3
The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous dat…
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
Chongyu Qu, Tiezheng Zhang, Hualin Qiao +4
Annotating medical images, particularly for organ segmentation, is laborious and time-consuming. For example, annotating an abdominal organ requires an estimated rate of 30-60 minu…
Label-Free Liver Tumor Segmentation
Qixin Hu, Yixiong Chen, Junfei Xiao +4
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intrig…
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen +7
An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem…
Synthetic Tumors Make AI Segment Tumors Better
Qixin Hu, Junfei Xiao, Yixiong Chen +4
We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and tex…
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
Junfei Xiao, Lequan Yu, Zongwei Zhou +4
Batch normalization (BN) uniformly shifts and scales the activations based on the statistics of a batch of images. However, the intensity distribution of the background pixels ofte…
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh +4
Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we…
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee +4
Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this parad…