Titlebook: Cancer Prevention Through Early Detection; Second International Sharib Ali,Fons van der Sommen,Iris Kolenbrander Conference proceedings 202

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https://doi.org/10.1007/978-3-031-45350-2medical image analysis; machine learning; deep learning; lesion classification; lesion detection; lesion

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A Deep Attention-Multiple Instance Learning Framework to?Predict Survival of?Soft-Tissue Sarcoma frotted from the Deep Attention-MIL model are used to divide the patients into low/high-risk groups and predict survival time. The framework was trained and validated on a local dataset including 220 patients, then it was used to predict the survival for 48 patients in an external validation dataset. T

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Fully Automated CAD System for?Lung Cancer Detection and?Classification Using 3D Residual U-Net withxtensive experimental results illustrate the effectiveness of our 3D residual U-Net model. These results demonstrate the exceptional detection performance achieved by our proposed model with a sensitivity of 97.65% and an average classification accuracy of 96.37%. Performance analysis demonstrates t

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Multispectral 3D Masked Autoencoders for?Anomaly Detection in?Non-Contrast Enhanced Breast MRI-cancerous images are presented to the model, with the purpose of localizing anomalous tumor regions during test time. We use a public dataset for model development. Performance of the architecture is evaluated in reference to subtraction images created from DCE-MRI. Code has been made publicly avai

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ColNav: Real-Time Colon Navigation for?Colonoscopyure, providing actionable and comprehensible guidance to un-surveyed areas in real-time, while seamlessly integrating into the physician’s workflow. Through coverage experimental evaluation, we demonstrated that our system resulted in a higher polyp recall (PR) and high inter-rater reliability with