# External resources ## Alzheimer’s disease ### Clinical context * [Alzheimer's association](https://www.alz.org/alzheimer_s_dementia) * [Advances in Alzheimer's Disease: Imaging and Biomarker Research](https://www.youtube.com/watch?v=7J3-59mRcxk) (Video by Dr Philip Scheltens) * [Imaging biomarkers in Alzheimer's disease](http://www.sciencedirect.com/science/article/pii/B978012816176000020X) (Book chapter by Dr Carole Sudre et al.) ### Public datasets * [Alzheimer's Disease Neuroimaging Initiative](http://adni.loni.usc.edu) * [Australian Imaging Biomarkers and Lifestyle](https://aibl.csiro.au/adni/index.html) * [Open Access Series of Imaging Studies](https://www.oasis-brains.org) ## Deep learning ### Courses * [Introduction to Deep Learning](http://introtodeeplearning.com/) by MIT * [Deep Learning](https://www.deeplearning.ai/) by Andrew Ng * [Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) by Stanford University ### Books * [Deep Learning](https://www.deeplearningbook.org) by Ian Goodfellow, Yoshua Bengio and Aaron Courville * [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python) by François Chollet * [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) by Aurélien Géron ### Convolutional neural networks * [AlexNet](https://dl.acm.org/doi/10.1145/3065386) * [VGGNet](https://arxiv.org/pdf/1409.1556.pdf) * [Inception](https://arxiv.org/pdf/1409.4842.pdf) * [ResNet](https://arxiv.org/pdf/1512.03385.pdf) * [Xception](https://arxiv.org/pdf/1610.02357.pdf) ### Generative models * [Variational Autoencoder](https://arxiv.org/pdf/1312.6114.pdf) * [Generative Adversarial Network](https://arxiv.org/pdf/1406.2661.pdf) * [Conditional Generative Adversarial Network](https://arxiv.org/pdf/1411.1784.pdf) * [Cycle Generative Adversarial Network](https://arxiv.org/pdf/1703.10593.pdf) ### Recurrent neural networks * [Vanilla Recurrent Neural Network](https://www.nature.com/articles/323533a0) * [Long Short-Term Memory](https://www.mitpressjournals.org/doi/abs/10.1162/neco.1997.9.8.1735?journalCode=neco) * [Gated Recurrent Unit](https://arxiv.org/pdf/1406.1078.pdf) ## Software ### Neuroimaging * [FreeSurfer](http://freesurfer.net): An open source software suite for processing and analyzing (human) brain MRI images * [Statistical Parametric Mapping](https://www.fil.ion.ucl.ac.uk/spm/): Analysis of Brain Imaging Data Sequences * [FMRIB Software Library](https://surfer.nmr.mgh.harvard.edu/fswiki/FSL): A fMRI, MRI and DTI analysis software] * [Nipype](https://nipype.readthedocs.io): Neuroimaging in Python - Pipelines and Interfaces * [BIDS Apps](https://bids-apps.neuroimaging.io/apps/): Portable neuroimaging pipelines that understand BIDS datasets * [Clinica](http://www.clinica.run): A software platform for clinical neuroimaging studies ### Data analysis * [PyTorch](https://pytorch.org): An Imperative Style, High-Performance Deep Learning Library * [TensorFlow](https://www.tensorflow.org): Large-Scale Machine Learning on Heterogeneous Systems * [scikit-learn](https://scikit-learn.org): Machine Learning in Python * [Clinica](http://www.clinica.run): A software platform for clinical neuroimaging studies * [ClinicaDL](https://clinicadl.readthedocs.io): A framework for the reproducible classification of Alzheimer's disease using deep learning # Deep learning for computer-aided diagnosis from images

# Medical image synthesis with deep learning