The deep learning textbook Goodfellow, Ian. et al., (2016). Deep Learning. MIT press.
This is the reference book for this part of the module The module will assume prior knowledge of basic machine learning, covered in Part I of the book. The lectures will go through the majoirty of Part II and selective topics in Part III, with an introduction to latest research topics in the fiels and applications in medical imaging.
Basic machine learning textbooks Hastie et al., The Elements of Statistical Learning. Springer
It is used in the first part of the module.
Bishop, C.M., (2006). Pattern Recognition and Machine Learning. Springer
For the basic machine learning referecnce, with consistent mathematical notations to the Deep Learning book.
Selected medical imaging research papers and surveys Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sánchez, C.I., 2017. A survey on deep learning in medical image analysis. Medical image analysis, 42, pp.60-88.
Medical image segmentation Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C., 2018. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE transactions on medical imaging, 37(8), pp.1822-1834.
Medical image registration de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M. and Išgum, I., 2017. End-to-end unsupervised deformable image registration with a convolutional neural network. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 204-212). Springer, Cham.
Hu, Y., Modat, M., Gibson, E., Li, W., Ghavami, N., Bonmati, E., Wang, G., Bandula, S., Moore, C.M., Emberton, M. and Ourselin, S., 2018. Weakly-supervised convolutional neural networks for multimodal image registration. Medical image analysis, 49, pp.1-13.
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J. and Dalca, A.V., 2019. VoxelMorph: a learning framework for deformable medical image registration. IEEE transactions on medical imaging, 38(8), pp.1788-1800.
De Fauw, J., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D. and van den Driessche, G., 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine, 24(9), pp.1342-1350.
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J. and Maier-Hein, K.H., 2020. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, pp.1-9.