RETRACTED ARTICLE: Semantic segmentation-based skin cancer detection

Renuka, N.

doi:10.1007/s00500-023-08557-3

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The Deep Image Prior approach introduced in [1,2] shows that generative neural architectures are capable to capture image statistics even in absence of a training phase and without using large datasets. The original work employed U-Net structures for tackling several imaging problems: indeed these architectures have gained interest since they showed to have remarkable performances in several tasks, such as image segmentation [20,21] in medical imaging, in image generation [22,23], precipitation nowcasting [24]. In this paper we take into account the same structure of [1,2]: for a visual inspection we refer to Fig.…”

Section: The Dip Neural Networkmentioning

confidence: 99%

Early stopping strategies in Deep Image Prior

Benfenati,

Catozzi,

Franchini

et al. 2024

Preprint

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The so-called Deep Image Prior approach is an unsupervised deep learning methodology which has gained great interest in recent years due to its effectiveness in tackling imaging problems. However, a well known drawback of Deep Image Prior is the need for a proper early stopping technique to prevent undesired corruptions in the reconstructed images. Determining the optimal number of iterations depends on both the specific application and the features of the images being processed. As a consequence, several numerical trials are typically required to decide when to stop the Deep Image Prior procedure, resulting in significant computational costs and time requirements. This paper aims to introduce two early stopping techniques for Deep Image Prior, based on different approaches and different perspectives. The first one relies on the Neural Architecture Search strategy by generating hyperparameters configurations for the neural network employed in Deep Image Prior, configurations that shall be able to provide clean images comparable to those obtained by the standard configuration, optimally stopped, but with significantly fewer iterations. The second proposed early stopping strategy is based on a modified version of the BRISQUE metric, a no-reference image quality measure, and it aims to track the behaviour of the PSNR curve, obtained by applying Deep Image Prior, without knowing the ground truth image. While the NAS-based early stopping technique is particularly suited in those situations where the computational time is limited, this latter one is also relevant when a larger number of iterations is allowed. Several numerical experiments on different denoising applications show a promising performance of Deep Image Prior combined with the suggested early stopping procedures.

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