Github: https://github.com/deeponcology/PyTorchMedicalAI
- https://github.com/deeponcology/applied-dl-2018
- The curriculum of the first 2018 course: http://deep-ml.com/assets/5daydeep/#/
Shlomo Kashani, Head of AI at DeepOncology AI, Kaggle Expert, Founder of Tel-Aviv Deep Learning Bootcamp: shlomo@deeponcology.ai
Knowledge of python programming Basics of linear algebra and statistics
Google Collab, Google Cloud, Python Jupyter
PyTorch is an open source deep learning framework that’s quickly become popular with AI researchers for its ease of use, clean Pythonic API, and flexibility. With the preview release of PyTorch 1.0, developers can now seamlessly move from exploration to production deployment using a single, unified framework.
- Google Collab setup
- Running a CUDA program in C from Python
- PyTorch Tensors on the GPU
- Basics of PyTorch Data Loaders
- Standard PyTorch Augmentations (Transforms)
- Writing custom PyTorch Augmentations (RandomErasing)
- SOTA Augmentation libraries (Albumentations)
- Histopathology Images
- Lab 01 (old version)- Melanoma Classification: https://github.com/bayesianio/applied-dl-2018/blob/master/lab-0-SeNet-SeedLings.ipynb and https://bayesian-ai.trydiscourse.com/t/12-applied-deep-learning-labs-1-melanoma-detection/20
- Lab 02 (old version)- Breast Cancer Classification: https://github.com/bayesianio/applied-dl-2018/blob/master/lab-2-Breast-Cancer-Histopathology-SeNet.ipynb and https://bayesian-ai.trydiscourse.com/t/12-applied-deep-learning-labs-2-breast-cancer-classification/21
Deep learning, a sub-domain of machine learning, has lately showed amazing results across an assortment of domains. Biology and medicine are data affluent, but the data is involved and frequently ill-understood. Problems of this quality may be especially well-suited to deep learning methods.
This is a provisional curriculum, which is subject to change without notice.
- Ubuntu Linux 16.04, Mac OSX or Windows 10
- Python 3.5+ or above
- CUDA 9.2 drivers.
- cuDNN 7.0.
- PyTorch and torchvision wheels are available on http://pytorch.org
- pytorch >= 0.4.0
- torchvision
- Pillow
- scipy
- tqdm
Keep in mind that this repository expects data to be in same format as Imagenet. I encourage you to use your own datasets. In that case you need to organize your data such that your dataset folder has EXACTLY two folders. Name these 'train' and 'val'
The 'train' folder contains training set and 'val' fodler contains validation set on which accuracy / log loss is measured.
The structure within 'train' and 'val' folders will be the same. They both contain one folder per class. All the images of that class are inside the folder named by class name; this is crucial in PyTorch.
If your dataset has 2 classes like in the Kaggle Statoil set, and you're trying to classify between pictures of 1) ships 2) Icebergs, say you name your dataset folder 'data_directory'. Then inside 'data_directory' will be 'train' and 'test'. Further, Inside 'train' will be 2 folders - 'ships', 'icebergs'.
|- data_dir
|- train
|- ships
|- ship_image_1
|- ship_image_2
.....
|- ice
|- ice_image_1
|- ice_image_1
.....
|- val
|- ships
|- ice
For a full example refer to: https://github.com/QuantScientist/Deep-Learning-Boot-Camp/blob/master/Kaggle-PyTorch/PyTorch-Ensembler/kdataset/seedings.py