A deep learning self-attention cross residual network with Info-WGANGP for mitotic cell identification in HEp-2 medical microscopic images

Anaam, Asaad; Al-antari, Mugahed A.; Gofuku, Akio

doi:10.1016/j.bspc.2023.105191

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…Finding out which parts of the body are impacted by the illness is the main goal of medical image interpretation to help doctors understand how lesions grow. Four steps-image preprocessing, segmentation, feature extraction, and pattern detection and classification-make up most of the examination of a medical image [36][37][38]. Preprocessing is used to fix undesired image defects or to enhance image data for later processing.…”

Section: Review Background 21 Medical Image Analysismentioning

confidence: 99%

“…There are also additional threat models, such as the black-box attack, which in real-life situations presents a bigger security risk to computer-aided diagnostic models [12,43]. Several adversarial defense mechanisms have been established to defend deep learning models from adversarial attacks [38,39,44]. The most popular among them is adversarial training [42,45], which can increase inherent network resilience by supplementing adversarial cases as training data.…”

Section: Medical Image Analysismentioning

confidence: 99%

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A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

Muoka,

Yi,

Ukwuoma

et al. 2023

Mathematics

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Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

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Section: Review Background 21 Medical Image Analysismentioning

confidence: 99%