Contributors

Covers: theory of Deep Learning

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

0 comment

Coming soon

public

Contributors

- Objectives
- This recipe is your starting point to understand why and how deep learning works.
- Potential Use Cases
- Image processing and computer vision, Natural language processing
- Who is This For ?
- BEGINNERData Scientists, Machine Learning Engineers

Click on each of the following **annotated items** to see details.

Resources

VIDEO 1. Introduction: Pytorch and Linear Algebra

- Why does deep learning work?
- Why PyTorch?
- Why linear algebra?

20 minutes

RECIPE 2. Foundations of Algebra for Deep Learning

2 hours

RECIPE 3. Deep Learning Model Training and Optimization

3 hours

RECIPE 4. Extracting Features using Convolution

3 hours

VIDEO 5. Short intro of Conv Nets

- What is the convolution operation?
- What are the filters inside the CNN?

5 minutes

PAPER 6. Deep learning with convolutional neural network in radiology

10 minutes

0 comment