CT images GAN-based augmentation with AdaIN for lung nodules detection

Kryuchkov, Maksim; Khanzhina, Natalia; Osmakov, I. A.; Ul’yanov, P. G.

doi:10.1117/12.2587940

Cited by 1 publication

(1 citation statement)

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“…Conditional GANs are used by Kyuchkov et al [34] on CT images to improve the reproducibility and detection probability of pulmonary nodules. For data augmentation, the model used an open-source CT-GAN network, Wasserstein loss, and adaptive instance normalization.…”

Section: B Unavailability and Imbalanced Datamentioning

confidence: 99%

Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust

Dhar

Dey²,

Borra

et al. 2023

IEEE Trans. Technol. Soc.

107

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Deep learning has revolutionized the detection of diseases and is helping the healthcare sector break barriers in terms of accuracy and robustness to achieve efficient and robust computer-aided diagnostic systems. The application of deep learning techniques empowers automated AI-based utilities requiring minimal human supervision to perform any task related to medical diagnosis of fractures, tumors, and internal hemorrhage; preoperative planning; intra-operative guidance, etc. But deep learning faces some major threats to the flourishing healthcare domain. This paper traverses the major challenges that the deep learning community of researchers and engineers faces, particularly in medical image diagnosis, like the unavailability of balanced annotated medical image data, adversarial attacks faced by deep neural networks and architectures due to noisy medical image data, a lack of trustability among users and patients, and ethical and privacy issues related to medical data. This study explores the possibilities of AI autonomy in healthcare by overcoming the concerns about trust that society has in autonomous intelligent systems.

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Section: B Unavailability and Imbalanced Datamentioning

confidence: 99%