A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

By S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, B. van Ginneken, Anant Madabhushi, Jerry L Prince, Daniel Rueckert, Ronald M. Summers. Proceedings of the IEEE, 2020

1,021 citations recorded by the source corpus.

Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high-performance computing. However, medical imaging presents unique challenges that confront DL approaches. In this survey article, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in DL are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, and so on. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c4c4a61edf42ed162651135dbf07e24b695ec2aa

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Citations in the corpus

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? (2020, methodology): “Recently, annotation-efficient methods, such as semi-supervised learning, weakly supervised learning, and continual learning, have received great attention in both computer vision and medical image analysis communities [6], [10], [11].”
Transformers in Medical Imaging: A Survey (2022, background): “However, due to inadequacy to leverage the expressive power of large medical imaging data sets, these hand-crafted models often suffer from poor discriminative capability [56].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “This is achieved by emerging advanced technologies, including DL [311], [312], Tactile Internet, IoT, edge AI, and wireless communications.”
Shallow Attention Network for Polyp Segmentation (2021, background): “, color and texture) [13,24,17], which can hardly capture the global context information and are not robust to complex scenarios.”
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification (2022, background): “Compared with natural images, medical images have diverse characteristics Zhou et al. [2021], fewer data, and different medical equipment, resulting in limited training data and models that cannot focus well on classiﬁcation features.”
Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions (2024, background): “This fundamental step is integral to numerous clinical applications, such as computer-aided diagnosis, treatment planning, and monitoring disease progression [1, 2].”