Search Results for author: Changhee Han

Found 13 papers, 0 papers with code

Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans

no code implementations3 Jun 2021 Akihiro Fukuda, Changhee Han, Kazumi Hakamada

Machine Learning-based fast and quantitative automated screening plays a key role in analyzing human bones on Computed Tomography (CT) scans.

Anomaly Detection Computed Tomography (CT) +1

Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation

no code implementations3 Jun 2021 Changhee Han

Convolutional Neural Networks (CNNs) can play a key role in Medical Image Analysis under large-scale annotated datasets.

Image Augmentation

Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey

no code implementations2 Jun 2021 Changhee Han, Takayuki Okamoto, Koichi Takeuchi, Dimitris Katsios, Andrey Grushnikov, Masaaki Kobayashi, Antoine Choppin, Yutaka Kurashina, Yuki Shimahara

Convolutional Neural Networks (CNNs) intrinsically requires large-scale data whereas Chest X-Ray (CXR) images tend to be data/annotation-scarce, leading to over-fitting.

MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction

no code implementations24 Jul 2020 Changhee Han, Leonardo Rundo, Kohei Murao, Tomoyuki Noguchi, Yuki Shimahara, Zoltan Adam Milacski, Saori Koshino, Evis Sala, Hideki Nakayama, Shinichi Satoh

Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices.

MRI Reconstruction Unsupervised Anomaly Detection

Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems

no code implementations12 Jan 2020 Changhee Han, Leonardo Rundo, Kohei Murao, Takafumi Nemoto, Hideki Nakayama

Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training.

Image Augmentation Image Generation

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

USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

no code implementations17 Apr 2019 Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga

The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.

Learning More with Less: GAN-based Medical Image Augmentation

no code implementations29 Mar 2019 Changhee Han, Kohei Murao, Shin'ichi Satoh, Hideki Nakayama

Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data.

Image Augmentation Object Detection

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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