|Topic:||Deep learning for automatic detection of multiple myeloma from CT images|
|Supervisor:||Prof. Dr. Ing. Jan Kybic , Jan Hering Dipl.-Math.|
|Description:||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.
|Bibliography:||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.
|Realization form:||SW projekt|
|Max.number of students:||6|