CLASSIC AND DEEP VISION FOR HEALTHCARE IMAGE ANALYSIS:

CURRENT STATUS, CHALLENGES AND OPPORTUNITIES

TUTORIAL @ACPR19, 26TH NOVEMBER - AUCKLAND, NEW ZEALAND

LOCATION: TBC [MAP] TIME: TBC

ORGANISING TEAM

UNIVERSITY OF CAMBRIDGE

CITY UNIVERSITY OF HONG KONG

UNIVERSITY OF LIVERPOOL

NATIONAL UNIVERSITY OF SINGAPORE (NUS)

UNIVERSITY OF CAMBRIDGE

OVERVIEW


The advent of Deep Learning (DL) in computer vision – since the pioneering work of Hinton in 2012 – changed the perspective of the community, adopting in this way DL as the go-to technique for different computer vision tasks for medical image analysis. This emergence is justified by DL’s superior performance on various vision tasks including classification, recognition and image segmentation. However, despite the fact that DL is a powerful tool, there is still a lack of understanding of its mechanisms and decision strategies, which is not the case with more classical computer vision approaches as they are more tractable, and often offer a clear understanding about how they work. This interpretability gap is an important topic, especially in decision-sensitive applications like medical imaging, where reliability and explainability of solutions are essential in decision making.

In this tutorial, we will start by drawing attention to the current state of developments for healthcare image analysis. We will start by introducing the topic and giving an overview of recent developments in the area. We will then present current challenges when dealing with medical data focusing on three major topics: learning with unlabelled data, interpretable machine learning and transfer learning. We will close our tutorial by summarising the current challenges and opportunities in this domain. Some open questions related to the topic will also be discussed in the end.

SCHEDULE

PART 1: From Classic to Deep Vision for Medical Imaging – An Overview

1a. Tutorial Overview -

1b. Classic vs Deep Vision - Chan, Raymond H

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PART 2: In the Era of Machine Learning For Healthcare

2a. The Power of Unlabelled Data for Learning with Medical Data - Aviles-Rivero, Angelica I

2b. Interpretable Machine Learning for Medical Image Analysis - Chen, Ke

3c. Transfer Learning: From synthetic to real clinical data - Ji, Hui

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PART 2: What is Beyond Deep Learning in Medical Image Analysis?

2a. What is Beyond Deep Learning in Medical Image Analysis? Challenges and Opportunities - Schönlieb, Carola-Bibiane

MATERIALS

Tutorial Flyer [Download]

PART 1a. [Download]

PART 1b. Chan, RH [Download]

PART 2a. Aviles-Rivero, AI [Download]

PART 2b. Chen, K [Download]

PART 2c. Ji, H [Download]

PART 3. Schönlieb, C-B [Download]