7 citations · 17 across the 8 of their papers we have counts for
9 papers
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Muhammad Imran, Jonathan R. Krebs, Vishal Balaji Sivaraman +60
Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aort…
Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More
Feng Wang, Yaodong Yu, Guoyizhe Wei +4
Since the introduction of Vision Transformer (ViT), patchification has long been regarded as a de facto image tokenization approach for plain visual architectures. By compressing t…
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency
Feng Wang, Timing Yang, Yaodong Yu +7
In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representation…
Mamba-R: Vision Mamba ALSO Needs Registers
Feng Wang, Jiahao Wang, Sucheng Ren +6
Similar to Vision Transformers, this paper identifies artifacts also present within the feature maps of Vision Mamba. These artifacts, corresponding to high-norm tokens emerging in…
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
Hongxu Jiang, Muhammad Imran, Teng Zhang +4
Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for…
A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
Amarjeet Kumar, Hongxu Jiang, Muhammad Imran +7
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high co…
RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration
Vishal Balaji Sivaraman, Muhammad Imran, Qingyue Wei +7
We introduce RetinaRegNet, a zero-shot image registration model designed to register retinal images with minimal overlap, large deformations, and varying image quality. RetinaRegNe…
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
Zhiheng Cheng, Qingyue Wei, Hongru Zhu +4
The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medic…
CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention
Muhammad Imran, Jonathan R Krebs, Veera Rajasekhar Reddy Gopu +9
Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches…