A 2021 update on cancer image analytics with deep learning

Abstract

Deep learning (DL)‐based interpretation of medical images has reached a critical juncture of expanding outside research projects into translational ones, and is ready to make its way to the clinics. Advances over the last decade in data availability, DL techniques, as well as computing capabilities have accelerated this journey. Through this journey, today we have a better understanding of the challenges to and pitfalls of wider adoption of DL into clinical care, which, according to us, should and will drive the advances in this field in the next few years. The most important among these challenges are the lack of an appropriately digitized environment within healthcare institutions, the lack of adequate open and representative datasets on which DL algorithms can be trained and tested, and the lack of robustness of widely used DL training algorithms to certain pervasive pathological characteristics of medical images and repositories. In this review, we provide an overview of the role of imaging in oncology, the different techniques that are shaping the way DL algorithms are being made ready for clinical use, and also the problems that DL techniques still need to address before DL can find a home in clinics. Finally, we also provide a summary of how DL can potentially drive the adoption of digital pathology, vendor neutral archives, and picture archival and communication systems. We caution that the respective researchers may find the coverage of their own fields to be at a high‐level. This is so by design as this format is meant to only introduce those looking in from outside of deep learning and medical research, respectively, to gain an appreciation for the main concerns and limitations of these two fields instead of telling them something new about their own. This article is categorized under: Technologies > Artificial Intelligence Algorithmic Development > Biological Data Mining

Editorial: The use of deep learning in mapping and diagnosis of cancers

“…DL can be used for classifying a lesion into benign or malignant, for treatment response evaluation and survival prediction. If DL models can be trained using a large dataset from a source domain, then it can be used in a target domain with a small sample size ( 2 ).…”

“…DL can mark specific areas of concern on an image and assist the radiologists in decision making ( 2 ).…”

“…Images acquired at different times can be accurately linked using DL, thus, enabling the radiologists to compare the images ( 2 ).…”

“…There is need for equal representation of data on training and test sets failing which data gets skewed and bias is introduced ( 2 ).…”

“…Convolutional Neural Network (CNN) is the most commonly used network of DL, which contains multiple layers, with weighted connections between neurons that are trained iteratively to improve performance. DL can be supervised or unsupervised, but most of the practical uses of DL in cancer has been with supervised learning where labelled images are used for data training ( 2 ). Despite the growing number of uses of DL in cancer mapping and diagnosis, there are uncharted territories in DL which remain to be explored to utilize it to its full capacity.…”