activity
20202026
most citedModels Genesis

328 citations · 356 across the 28 of their papers we have counts for

collaborators
Showing eess.IVShow all

11 papers · 1 filter

eess.IV2025★ 4 cited

How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?

Wenxuan Li, Alan Yuille, Zongwei Zhou

The pre-training and fine-tuning paradigm has become prominent in transfer learning. For example, if the model is pre-trained on ImageNet and then fine-tuned to PASCAL, it can sign…

eess.IV2025

RadGPT: Constructing 3D Image-Text Tumor Datasets

Pedro R. A. S. Bassi, Mehmet Can Yavuz, Kang Wang +7

Cancers identified in CT scans are usually accompanied by detailed radiology reports, but publicly available CT datasets often lack these essential reports. This absence limits the…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024★ 3 cited

Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis

Yuanhao Cai, Yixun Liang, Jiahao Wang +5

X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on N…

eess.IV2023★ 2 cited

Structure-Aware Sparse-View X-ray 3D Reconstruction

Yuanhao Cai, Jiahao Wang, Alan Yuille +2

X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light. Yet, existing neural radi…