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- Title
Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities.
- Authors
Apostolopoulos, Ioannis D.; Papathanasiou, Nikolaos D.; Papandrianos, Nikolaos I.; Papageorgiou, Elpiniki I.; Panayiotakis, George S.
- Abstract
Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models.
- Subjects
CONVOLUTIONAL neural networks; COMPUTER-assisted image analysis (Medicine); DEEP learning; DIAGNOSTIC imaging; OBJECT recognition (Computer vision); FEATURE extraction; SIGNAL convolution; FORMATIVE tests
- Publication
Diagnostics (2075-4418), 2022, Vol 12, Issue 10, p2333
- ISSN
2075-4418
- Publication type
Article
- DOI
10.3390/diagnostics12102333