Blood Vessel Enhancement in Liver Region from a Sequence of Angiograms Taken under Free Breathing

Morio, Kawabe; Kokura, Yuri; Ohnishi, Takashi; Nakano, K.; Kato, Harumi; Ooka, Yoshihiko; Sakai, Tomoya; Haneishi, Hideaki

doi:10.1007/978-981-15-3651-9_13

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2021

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Cited by 2 publications

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“…Although the U-Net in the experiment may overfit the scene, we expect that it will generalize well if it experiences similar foreground objects in various training sequences. Our unsupervised deep learning would be advantageous when applied to medical images with L+S structure, e.g., angiography under freebreathing condition [27].…”

Section: Methodsmentioning

confidence: 99%

Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priors

Sakai

2021

Preprint

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What if deep neural networks can learn from sparsity-inducing priors? When the networks are designed by combining layer modules (CNN, RNN, etc), engineers less exploit the inductive bias, i.e., existing well-known rules or prior knowledge, other than annotated training data sets. We focus on employing sparsity-inducing priors in deep learning to encourage the network to concisely capture the nature of high-dimensional data in an unsupervised way. In order to use non-differentiable sparsity-inducing norms as loss functions, we plug their proximal mappings into the automatic differentiation framework. We demonstrate unsupervised learning of U-Net for background subtraction using low-rank and sparse priors. The U-Net can learn moving objects in a training sequence without any annotation, and successfully detect the foreground objects in test sequences.

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Section: Methodsmentioning

confidence: 99%