A Generative Adversarial Network Approach to Metastatic Cancer Cell Images

Lee, Seohyun; Kim, Hyuno; Higuchi, Hideo; Ishikawa, Masatoshi; Natato, Ryuichiro

doi:10.1109/icaiic54071.2022.9722664

Cited by 1 publication

(1 citation statement)

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“…The infrared image generated in this paper is required for later object detection, and the quality of the image has an impact on the object detection result. As a result, this paper uses FID to evaluate the quality and diversity of generated infrared images based on SSIM and PSNR evaluation metrics [47]. FID is a statistical aspect of the computer vision features of the original image used to measure the similarity between the two sets of images.…”

Section: ) Evaluation Metricsmentioning

confidence: 99%

A Ship Detection Method in Infrared Remote Sensing Images Based on Image Generation and Causal Inference

Zhang,

Li,

et al. 2024

Electronics

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Aiming at the problems of scarce public infrared ship data and the difficulty of obtaining them, a ship image generation method based on improved StyleGAN2 is proposed. The mapping network in StyleGAN2 is replaced with a Variational Auto-Encoder, enabling the generated latent variables to retain original image information while reducing computational complexity. This benefits the construction of the image. Additionally, a self-attention mechanism is introduced to capture dependency information between distant features, generating more detailed object representation. By reducing the number of input noises in the generator, the quality of the generated images is effectively enhanced. Experimental results show that the images generated by the proposed method closely resemble the structure, content and data distribution of the original real images, achieving a higher level of detail. Regarding ship detection methods based on deep learning, they often suffer from complex detection networks, numerous parameters, poor interpretability, and limited real-time performance. To address these issues, a lightweight multi-class ship detection method for infrared remote sensing images is designed. This method aims to improve real-time performance while maintaining accurate ship detection. Based on ship detection, an interpretable ship detection approach based on causal reasoning is presented. By integrating singular value decomposition with the Transformer architecture, the model focuses on causal ship features associated with labels in the images. This enhances the model’s robustness against non-causal information, such as background details, and improves its interpretability.

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Section: ) Evaluation Metricsmentioning

confidence: 99%