Data synthesis and adversarial networks
| Autor: | Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Radiological and Ultrasound Technology Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition Health Informatics Electrical Engineering and Systems Science - Image and Video Processing Computer Graphics and Computer-Aided Design Machine Learning (cs.LG) Artificial Intelligence (cs.AI) SDG 3 - Good Health and Well-being FOS: Electrical engineering electronic engineering information engineering Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition |
| Zdroj: | Medical Image Analysis. 84 |
| ISSN: | 1361-8415 |
| DOI: | 10.1016/j.media.2022.102704 |
| Popis: | Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in Generative Adversarial Networks (GANs), data synthesis, and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community. v2, 51 pages, 15 Figures, 9 Tables, accepted for publication in Medical Image Analysis |
| Databáze: | OpenAIRE |
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