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Št. zadetkov: 3
Uncertainty quantification via localized gradients for deep learning-based medical image assessments
Brayden Schott, Dmitry Pinchuk, Victor Santoro Fernandes, Žan Klaneček, Luciano Rivetti, Alison Deatsch, Scott Perlman, Yixuan Li, Robert Jeraj
Izvirni znanstveni članek
Oznake: medicinska fizika;medicinsko slikanje;tumorji;globoko učenje;medical physics;medical imaging;tumors;deep learning;
Objective. Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models ar ...
Leto: 2024 Vir: Fakulteta za matematiko in fiziko (UL FMF)
Sensitivity of a deep-learning-based breast cancer risk prediction model
Žan Klaneček, Yao Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Brayden Schott, Alison Deatsch, Andrej Studen, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Robert Jeraj
Izvirni znanstveni članek
Oznake: rak dojke;tveganja;globoko ležeči tumorji;mamografija;breast cancer risk;deep learning;mammography;
Objective. When it comes to the implementation of deep-learning based breast cancer risk (BCR) prediction models in clinical settings, it is important to be aware that these models could be sensitive to various factors, especially those arising from the acquisition process. In this work, we investig ...
Leto: 2025 Vir: Fakulteta za matematiko in fiziko (UL FMF)
Impact of pectoral muscle removal on deep-learning-based breast cancer risk prediction
Žan Klaneček, Yao Kuan Wang, Tobias Wagner, Lesley Cockmartin, Nicholas Marshall, Brayden Schott, Alison Deatsch, Andrej Studen, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Hilde Bosmans, Robert Jeraj
Izvirni znanstveni članek
Oznake: dejavniki tveganja;rak dojke;kalibracija;mamografija;breast cancer risk;calibration;mammography;
State-of-the-art Breast Cancer Risk (BCR) prediction models have been originally trained on mammograms with pectoral muscle (PM) included. This study investigated whether excluding PM during training/fine-tuning improves the model's BCR discrimination performance, calibration, and robustness.&xD;&xD ...
Leto: 2025 Vir: Fakulteta za matematiko in fiziko (UL FMF)
Št. zadetkov: 3
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