Minah Han

According to our database, Minah Han authored at least 9 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2022

Perceptual CT Loss: Implementing CT Image Specific Perceptual Loss for CNN-Based Low-Dose CT Denoiser.

Minah Han, Hyunjung Shim, Jongduk Baek

IEEE Access, 2022

Low-dose CT denoising via CNN trained using images with activation map.

Minah Han, Jongduk Baek

Proceedings of the Medical Imaging 2022: Image Perception, 2022

A convolutional neural network based super resolution technique of CT image utilizing both sinogram domain and image domain data.

Minwoo Yu, Minah Han, Jongduk Baek

Proceedings of the Medical Imaging 2022: Image Processing, 2022

2020

A Convolutional Neural Network-Based Anthropomorphic Model Observer for Signal Detection in Breast CT Images Without Human-Labeled Data.

Byeongjoon Kim, Minah Han, Jongduk Baek

IEEE Access, 2020

Convolutional neural network-based anthropomorphic model observer for breast cone-beam CT images.

Byeongjoon Kim, Minah Han, Jongduk Baek

Proceedings of the Medical Imaging 2020: Image Perception, 2020

A performance comparison of convolutional neural network based anthropomorphic model observer and linear model observer for signal-known statistically detection tasks.

Minah Han, Byeongjoon Kim, Jongduk Baek

Proceedings of the Medical Imaging 2020: Image Perception, 2020

2019

Implementation of an ideal observer model using convolutional neural network for breast CT images.

Gihun Kim, Minah Han, Hyunjung Shim, Jongduk Baek

Proceedings of the Medical Imaging 2019: Image Perception, 2019

2018

Lesion detection performance of cone beam CT images with anatomical background noise: single-slice vs multi-slice human and model observer study.

Minah Han, Hanjoo Jang, Jongduk Baek

Proceedings of the Medical Imaging 2018: Image Perception, 2018

2016

Effect of anatomical backgrounds on detectability in volumetric cone beam CT images.

Minah Han, Subok Park, Jongduk Baek

Proceedings of the Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 27 February, 2016