Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.03941v1</id>\n    <title>Slice-wise quality assessment of high b-value breast DWI via deep learning-based artifact detection</title>\n    <updated>2026-03-04T11:00:42Z</updated>\n    <link href='https://arxiv.org/abs/2603.03941v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.03941v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Diffusion-weighted imaging (DWI) can support lesion detection and characterization in breast magnetic resonance imaging (MRI), however especially high b-value diffusion-weighted acquisitions can be prone to intensity artifacts that can affect diagnostic image assessment. This study aims to detect both hyper- and hypointense artifacts on high b-value diffusion-weighted images (b=1500 s/mm2) using deep learning, employing either a binary classification (artifact presence) or a multiclass classification (artifact intensity) approach on a slice-wise dataset.This IRB-approved retrospective study used the single-center dataset comprising n=11806 slices from routine 3T breast MRI examinations performed between 2022 and mid-2023. Three convolutional neural network (CNN) architectures (DenseNet121, ResNet18, and SEResNet50) were trained for binary classification of hyper- and hypointense artifacts. The best performing model (DenseNet121) was applied to an independent holdout test set and was further trained separately for multiclass classification. Evaluation included area under receiver operating characteristic curve (AUROC), area under precision recall curve (AUPRC), precision, and recall, as well as analysis of predicted bounding box positions, derived from the network Grad-CAM heatmaps. DenseNet121 achieved AUROCs of 0.92 and 0.94 for hyper- and hypointense artifact detection, respectively, and weighted AUROCs of 0.85 and 0.88 for multiclass classification on single-slice high b-value diffusion-weighted images. A radiologist evaluated bounding box precision on a 1-5 Likert-like scale across 200 slices, achieving mean scores of 3.33+-1.04 for hyperintense artifacts and 2.62+-0.81 for hypointense artifacts. Hyper- and hypointense artifact detection in slice-wise breast DWI MRI dataset (b=1500 s/mm2) using CNNs particularly DenseNet121, seems promising and requires further validation.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <published>2026-03-04T11:00:42Z</published>\n    <arxiv:primary_category term='cs.CV'/>\n    <author>\n      <name>Ameya Markale</name>\n    </author>\n    <author>\n      <name>Luise Brock</name>\n    </author>\n    <author>\n      <name>Ihor Horishnyi</name>\n    </author>\n    <author>\n      <name>Dominika Skwierawska</name>\n    </author>\n    <author>\n      <name>Tri-Thien Nguyen</name>\n    </author>\n    <author>\n      <name>Hannes Schreiter</name>\n    </author>\n    <author>\n      <name>Shirin Heidarikahkesh</name>\n    </author>\n    <author>\n      <name>Lorenz A. Kapsner</name>\n    </author>\n    <author>\n      <name>Michael Uder</name>\n    </author>\n    <author>\n      <name>Sabine Ohlmeyer</name>\n    </author>\n    <author>\n      <name>Frederik B Laun</name>\n    </author>\n    <author>\n      <name>Andrzej Liebert</name>\n    </author>\n    <author>\n      <name>Sebastian Bickelhaupt</name>\n    </author>\n  </entry>"
}