Parallel Training Using a CNN-DNN Architecture for Accelerated Development of Diagnostic Models
Paper recorded by Signals 4 on 2026-09-11 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14
Category: cs.CV · 计算机视觉 · first seen 2026-09-14
Abstract
Artificial intelligence has shown promise in assisting radiologists in imaging-based diagnosis across a wide range of diseases. Efficient training of large deep learning models is essential to cope with extremely large data sets or dynamically growing disease data, like in a pandemic like situation. In this retrospective study, we collected 300 CT scans from COVID-19 and non-COVID-19 pneumonia pat
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Cite this page: Parallel Training Using a CNN-DNN Architecture for Accelerated Development of Diagnostic Models: the #71 most recent of 237 cs.CV papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/parallel-training-using-a-cnn-dnn-architecture-for-accelerated-development-of-di.html
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