Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets. Machine learning algorithms tend to perform better within the setting wherein they are trained, a phenomenon known as the domain effect.
Apr 4, 2022
In this work, we present evidence of a scanner and magnet strength specific domain effect for a deep-U-Net trained to segment spinal canals on axial MR images.
Evidence of a scanner and magnet strength specific domain effect for a deep-U-Net trained to segment spinal canals on axial MR images is presented and it is ...
Ensemble. Conference Paper. Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets. April 2022. DOI:10.1117 ...
We propose a novel approach to the semantic segmentation of medical images. In this study, a new sampling method to handle class imbalance in the medical ...
Missing: scanner effects
Sep 5, 2022 · This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological ...
In addition to CNNs, attention-based models and U-Nets have proven useful for analyzing lung CT scans. Because U-Nets can precisely localize images and capture ...
Jul 23, 2023 · In this chapter, we present the basic ideas for deep learning-based segmentation as well as some current state-of-the-art approaches, organized by supervision ...
May 27, 2024 · U-Net++ presents notable strengths in image segmentation with its ability to enhance accuracy through nested skip connections, capturing multi- ...
Missing: mitigates scanner effects
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We applied nnU-Net (no new U-Net), an automatically adapted deep convolutional neural network based on U-Net, to segment the bladder, rectum and HRCTV on CT ...