97 citations · 140 across the 35 of their papers we have counts for
8 papers
Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
Hanyu Zheng, Quan Liu, Ivan I. Kravchenko +3
Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically nece…
Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
Yinchi Zhou, Ho Hin Lee, Yucheng Tang +6
With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in disti…
An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level
Haoju Leng, Ruining Deng, Zuhayr Asad +4
Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous…
An End-to-end Pipeline for 3D Slide-wise Multi-stain Renal Pathology Registration
Peize Li, Ruining Deng, Yuankai Huo
Tissue examination and quantification in a 3D context on serial section whole slide images (WSIs) were laborintensive and time-consuming tasks. Our previous study proposed a novel…
Exploring shared memory architectures for end-to-end gigapixel deep learning
Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios +8
Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development of new models has been the saturation of GPU…
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging
Ruining Deng, Can Cui, Quan Liu +13
The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed an…