| Téma: | Deep learning for automatic detection of multiple myeloma from CT images
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| Vedoucí: | prof. Dr. Ing. Jan Kybic , Jan Hering Dipl.-Math.
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| Vypsáno jako: | Diplomová práce,Bakalářská práce,Individuální projekt,Dobrovolná odborná práce,Semestrální projektPráce v týmu a její organizace
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| Popis: | The task is to develop a deep learning (convolutional neural network) method to detect multiple myeloma in 3D CT images of long bones, especially femurs. Several network architectures should be tried and the performance compared with a classical solution. The particularity is that only weak annotations are possible - we know whether a subject is healthy or not but a precise location of the lesion is not available. This leads to so-called multiple instance learning methods.
Recommended implementation languages are Python or Julia.
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| Literatura: | J. Hering, J. Kybic, and L. Lambert, “Detecting multiple myeloma via generalized multiple-instance learning,” SPIE Medical Imaging 2018, p. 22.
F. Martínez-Martínez, J. Kybic, L. Lambert, and Z. Mecková, “Fully Automated Classification of Bone Marrow Infiltration in Low-Dose CT of Patients with Multiple Myeloma Based on Probabilistic Density Model and Supervised Learning,” Comput. Biol. Med., vol. 71, pp. 57–66, Apr. 2016.
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| Realizace: | SW projekt
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| Vypsáno dne: | 13.05.2019
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| Max. počet studentů: | 6
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| Přihlášení studenti: |
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