Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks von Baugh, Matthew, Tan, Jeremy, Müller, Johanna P., Dombrowski, Mischa, Batten, James, Kainz, Bernhard

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Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis von Shkëmbi, Glejdis, Müller, Johanna P., Li, Zhe, Breininger, Katharina, Schüffler, Peter, Kainz, Bernhard

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Confidence-Aware and Self-supervised Image Anomaly Localisation von P. Müller, Johanna, Baugh, Matthew, Tan, Jeremy, Dombrowski, Mischa, Kainz, Bernhard

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Image-Conditioned Diffusion Models for Medical Anomaly Detection von Baugh, Matthew, Reynaud, Hadrien, Marimont, Sergio Naval, Cechnicka, Sarah, Müller, Johanna P., Tarroni, Giacomo, Kainz, Bernhard

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Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks von Müller, Johanna P, Kainz, Bernhard

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nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods von Baugh, Matthew, Tan, Jeremy, Vlontzos, Athanasios, Müller, Johanna P., Kainz, Bernhard

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Resource-Efficient Medical Image Analysis with Self-adapting Forward-Forward Networks von Müller, Johanna P., Kainz, Bernhard

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Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks von Müller, Johanna P, Kainz, Bernhard

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Zero-Shot Anomaly Detection with Pre-trained Segmentation Models von Baugh, Matthew, Batten, James, Müller, Johanna P, Kainz, Bernhard

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Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis von Shkëmbi, Glejdis, Müller, Johanna P, Li, Zhe, Breininger, Katharina, Schüffler, Peter, Kainz, Bernhard

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Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks von Baugh, Matthew, Tan, Jeremy, Müller, Johanna P, Dombrowski, Mischa, Batten, James, Kainz, Bernhard

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Automatic Segmentation of Lymphatic Perfusion in Patients with Congenital Single Ventricle Defects von Stegmaier, Marietta, Müller, Johanna P., Schröder, Christian, Day, Thomas, Cuomo, Michela, Dewald, Oliver, Dittrich, Sven, Kainz, Bernhard

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Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability von Dombrowski, Mischa, Reynaud, Hadrien, Müller, Johanna P, Baugh, Matthew, Kainz, Bernhard

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Confidence-Aware and Self-Supervised Image Anomaly Localisation von Müller, Johanna P, Baugh, Matthew, Tan, Jeremy, Dombrowski, Mischa, Kainz, Bernhard

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Learnable Slice-to-volume Reconstruction for Motion Compensation in Fetal Magnetic Resonance Imaging von Jehn, Constantin, Müller, Johanna P., Kainz, Bernhard

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nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods von Baugh, Matthew, Tan, Jeremy, Vlontzos, Athanasios, Müller, Johanna P, Kainz, Bernhard

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Zero-Shot Anomaly Detection with Pre-trained Segmentation Models von Baugh, Matthew, Batten, James, Müller, Johanna P, Kainz, Bernhard

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Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability von Dombrowski, Mischa, Reynaud, Hadrien, Müller, Johanna P, Baugh, Matthew, Kainz, Bernhard

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Confidence-Aware and Self-Supervised Image Anomaly Localisation von Müller, Johanna P, Baugh, Matthew, Tan, Jeremy, Dombrowski, Mischa, Kainz, Bernhard

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Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks von Baugh, Matthew, Tan, Jeremy, Müller, Johanna P, Dombrowski, Mischa, Batten, James, Kainz, Bernhard

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