The document summarizes a presentation on deep learning in radiology. It provides an overview of deep learning concepts like artificial neural networks and convolutional neural networks. It discusses how deep learning can help with tasks in radiology like disease detection, classification of images as benign or malignant, segmentation of organs and lesions. The presentation also reviewed challenges of small medical image datasets and the future potential for deep learning algorithms to assist radiologists.

1. Iran Deep Learning Center
Paper presentation(Report):
Deep Learning in Radiology: An
Overview of the Concepts and a Survey
of the State of the Art With Focus on
MRI
Niloofar Sedighian Bidgoli
July 2020

2. What was covered:
 Review on Deep Learning concepts and its developments
 Branch of artificial intelligence where networks of simple interconnected units are used to extract
patterns from data in order to solve complex problems
 Artificial Neural Networks
 Convolutional Neural Networks
 The Learning Process for CNNs
 Deep Features
 Transfer Learning
 The Practice of Radiology
 One of the most challenging tasks in the interpretation of images is that of disease detection: the
rapid differentiation of abnormalities from normal background anatomy
 Deep Learning opportunities in Radiology:
 Nearly all of the primary data utilized in imaging as well as the outputs produced by radiologists
(ie, imaging reports) are digital, lending those data to analysis by artificial intelligence algorithms.

3. What was covered:
 Deep Learning in Radiology: State of the Art:
 Classification: classification of an image or an examination to determine the presence or
absence of an abnormality; classification of abnormalities as benign or malignant
 Segmentation: In MRI, the common applications are segmentation of organs, substructures, or
lesions
 Detection: an important step in the diagnostic process that identifies an abnormality (such as a
mass or a nodule), an organ, an anatomical structure
 Other Tasks in Radiology
 Image Registration
 Image Generation/Reconstruction
 Image Enhancement
 Content-Based Image Retrieval
 Objective Image Quality Assessment

4. What was covered:
 Main Challenges and Pitfalls
 While millions of training examples are available for problems related to natural images, the
datasets for medical images are typically much smaller
 Future of Deep Learning in Radiology
 some predict that deep-learning algorithms will replace radiologists (at least in their image
interpretation capacity) altogether