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1
A convolutional neural network-based model observer for breast CT images

Kim, Gihun; Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.47, no.4, pp.1619 - 1632, 2020-04

2
A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images

Kim, Byeongjoon; Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.46, no.9, pp.3906 - 3923, 2019-09

3
Implementation of an ideal observer model using convolutional neural network for breast CT images

Kim, Gihun; Han, Minah; Shim, Hyunjung; Baek, Jongduk, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, SPIE, 2019-02

4
Low-dose CT denoising via convolutional neural network with an observer loss function

Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.48, no.10, pp.5727 - 5742, 2021-10

5
Performance comparison of convolutional neural network based denoising in low dose CT images for various loss functions

Kim, Byeongjoon; Han, Minah; Shim, Hyunjung; Baek, Jongduk, Medical Imaging 2019: Physics of Medical Imaging, SPIE, 2019-02

6
Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images

Choi, Yunsu; Han, Minah; Jang, Hanjoo; Shim, Hyunjung; Baek, Jongduk, PLOS ONE, v.17, no.1, 2022-01

7
Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser

Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.50, no.5, pp.2787 - 2804, 2023-05

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