The Second Cancer Diagnostic Ultrasound Imaging with Deep Learning (CDUDL) Workshop

The 2^nd Cancer Diagnostic Ultrasound Imaging with Deep Learning (CDUDL) Workshop 2020, organised by School of Computing, took place virtually on 11^th October 2020. The CDUDL workshop is a forum for clinicians, researchers and doctoral candidates to discuss recent developments in artificial intelligence and deep learning techniques, and their application in automated analysis of ultrasound images to detect cancers.

The of number of ultrasound imaging research for cancer diagnostic is growing rapidly, and so are the deep learning technologies. The CDUDL aims to have a two-way knowledge exchange between a radiologist and computer scientist.

This year’s workshop, chaired by Dr Alaa AlZoubi, focused on advance topics in deep learning to automatically detect, segment and recognise cancers in ultrasound images.

Our guest speaker, Dr Bashir Al-Diri from The University of Lincoln delivered the first keynote focusing on retinal image analysis techniques and the possibilities of adapting them for tumour segmentation in ultrasound images. Doctor Yi-Cheng Zhu from Renmin Hospital (Shanghai), presented the second keynote highlighting the importance of using Imaging Reporting and Data Systems in clinical practice.

The workshop was an excellent opportunity for the six PhD candidates and the MSc students of Ten-D Buckingham Centre to present their most recent work and share their knowledge and experience with each other. With research from students covering areas such as topological aspects of convolutional layers and automatic thyroid nodule detection being presented.

At the end of the workshop, an open discussion was held with the keynote speakers and fellow researchers to discuss the research topics which were presented. Panellists actively debated one another and engaged the audience to help broaden understanding of the technologies and issues. A best presentation award was given to TenD MSc student, Mr Ali Eskandari for his research on Deep Convolutional Neural Network Models. With opportunities for students to attend workshops such as this becoming more frequent at the School of Computing, this encourages their development, preparing them for national and international conferences.

Dr Alaa AlZoubi said, “Despite the impact of Coronavirus – COVID-19, we are delighted to organise the second Cancer Diagnostic Ultrasound Imaging with Deep Learning (CDUDL, 2020) Workshop. Like many other workshops this year, CDUDL 2020 went virtual. Of course, it was a huge success with the participation of the keynote speakers, our research students and TenD.AI. We are aiming to organise the workshop on large scale next year, and we hope to see you all, in person, at CDUDL 2021”.

Mr Hongbo Du, TenD Buckingham Research and Development Centre Coordinator, also added that “This year’s CDUDL workshop was a great success. Our students showcased their most recent research and advances in deep learning for cancer diagnosis using ultrasound images. Despite the degrees of difficulty encountered and inconveniences caused by Covid-19, they all have made great contributions in solving difficult problems. Our research works in the field of medical image analysis in general and ultrasound image analysis in supporting accurate diagnosis of cancer in particular are extremely useful not only for advancing the machine learning technology but also benefiting the society by assisting early detection and hence more effective treatment of cancer. My gratitude goes to our funding partner TenD Innovations (TenD.AI) and hardworking members of staff and research students in the TenD Lab.”

Best Presentation winner and MSc student, Ali Eskandari said, “I am really grateful to have had the opportunity to attend the CDUDL 2020 workshop, and this was an amazing online experience for me and made me acquainted with the various topics and advanced research in medical image analysis. In addition, I would like to express my special thanks to Dr Alaa AlZoubi for organizing this workshop, as well as to the other professors present at the workshop. In the end, I am very delighted to win the best oral presentation award and I hope that this honour will be the beginning of more success.”