ArticleIEEE transactions on medical imaging2021
Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Article
- Pixelwise Gradient Model for Image Fusion (PGMIF): a multi-sequence magnetic resonance imaging (MRI) fusion model for tumor contrast enhancement of nasopharyngeal carcinoma.Quantitative imaging in medicine and surgery · 2024Article
- Article
- Meta semi-supervised medical image segmentation with label hierarchy.Health information science and systems · 2023Article
- Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.Medical physics · 2022Article
- Transfer learning for automatic joint segmentation of thyroid and breast lesions from ultrasound images.International journal of computer assisted radiology and surgery · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standard deep learning problem into a new neural network architecture with multi-output channels, which reflects different facets of the objective, and apply the deep neural network to improve the performance of image segmentation. By adding one or more interrelated auxiliary-output channels, we impose an effective consistency regularization for the main task of pixelated classification (i.e., image segmentation). Specifically, multi-output-channel consistency regularization is realized by residual learning via additive paths that connect main-output channel and auxiliary-output channels in the network. The method is evaluated on the detection and delineation of lung and liver tumors with public data. The results clearly show that multi-output-channel consistency implemented by residual learning improves the standard deep neural network. The proposed framework is quite broad and should find widespread applications in various deep learning problems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.