all AI news
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation. (arXiv:2203.10014v1 [cs.CV])
March 21, 2022, 1:11 a.m. | Kundan Kumar, Sumanshu Agarwal
cs.LG updates on arXiv.org arxiv.org
Extracting blood vessels from retinal fundus images plays a decisive role in
diagnosing the progression in pertinent diseases. In medical image analysis,
vessel extraction is a semantic binary segmentation problem, where blood
vasculature needs to be extracted from the background. Here, we present an
image enhancement technique based on the morphological preprocessing coupled
with a scaled U-net architecture. Despite a relatively less number of trainable
network parameters, the scaled version of U-net architecture provides better
performance compare to other methods …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Software Engineer, Data Platforms
@ Whatnot | San Francisco, CA, Los Angeles, CA, New York City, Phoenix, AZ, Seattle, WA, Denver, CO
Staff Data Engineer, Data Platform
@ Lilt | Indianapolis
Business Data Analyst - New Division
@ Breakthru Beverage Group | Toronto, ON, Canada
Data Operations Associate
@ iCapital | New York City, United States
Senior Data Scientist, R&D
@ Plusgrade | Toronto, Ontario