Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images. - Mohammad Momeny; Ali Asghar Neshat; Mohammad Arafat Hussain; Solmaz Kia; Mahmoud Marhamati; Ahmad Jahanbakhshi; Ghassan Hamarneh (Comput. Biol. Medicine, 2021)

Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy. - Mohammad Momeny; Ali Asghar Neshat; Abdolmajid Gholizadeh; Ahad Jafarnezhad; Elham Rahmanzadeh; Mahmoud Marhamati; Bagher Moradi; Ali Ghafoorifar; Yu-Dong Zhang 001 (Comput. Biol. Medicine, 2022)

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT. - Mohammad Arafat Hussain; Zahra Mirikharaji; Mohammad Momeny; Mahmoud Marhamati; Ali Asghar Neshat; Rafeef Garbi; Ghassan Hamarneh (Comput. Medical Imaging Graph., 2022)

Detection of citrus black spot disease and ripeness level in orange fruit using learning-to-augment incorporated deep networks. - Mohammad Momeny; Ahmad Jahanbakhshi; Ali Asghar Neshat; Ramazan Hadipour-Rokni; Yu-Dong Zhang 001; Yiannis Ampatzidis (Ecol. Informatics, 2022)

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images. - Adel Akbarimajd; Nicolas Hoertel; Mohammad Arafat Hussain; Ali Asghar Neshat; Mahmoud Marhamati; Mahdi Bakhtoor; Mohammad Momeny (J. Comput. Sci., 2022)