Search Results for author: Yujiro Furukawa

Found 3 papers, 0 papers with code

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

no code implementations31 May 2019 Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi

In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).

General Classification Image Augmentation +2

Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection

no code implementations29 Mar 2019 Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi

Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement.

Data Augmentation

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