Reconstructing Medical Images Using Generative Adversarial Networks: A Study

Buch, Phenilkumar; Thakkar, Amit

doi:10.1007/978-981-19-5037-7_6

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…At this point, the generator has learned the ability to generate images or text indistinguishable as real or fake (Figure 4). An example of a GAN showing the adversarial nature of generated images being scored by the discriminator as "fake" or "real" until the discriminator has been fooled, in essence creating fake images which can plausibly be considered as real [40].…”

Section: Applications Of Generative Adversarial Networkmentioning

confidence: 99%

“…IPMN and MCN are mucinous PCLs that can develop PC and account for approximately 8% of all PCs [4]. Timely surgical resection offers the only opportunity for curative An example of a GAN showing the adversarial nature of generated images being scored by the discriminator as "fake" or "real" until the discriminator has been fooled, in essence creating fake images which can plausibly be considered as real [40].…”

Section: Pancreatic Cystic Lesionsmentioning

confidence: 99%

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Deep Learning Applications in Pancreatic Cancer

Patel,

Zanos,

Hewitt

2024

Cancers

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Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging technology in the care of patients with pancreatic cancer.

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Section: Applications Of Generative Adversarial Networkmentioning

confidence: 99%

Section: Pancreatic Cystic Lesionsmentioning

confidence: 99%