Publications

Peer-reviewed journals

1. Hu Q, Giger ML. “Clinical Artificial Intelligence Applications: Breast Imaging.” Radiologic Clinics of North America 59(6) (2021): 1027-1043. Link;
2. Hu Q, Drukker K, Giger ML. “Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.” Journal of Medical Imaging 8(S1) (2021): 014503.Link;
3. El Naqa I, Li H, Fuhrman JD, Hu Q, Gorre N, Chen W, Giger ML. “Lessons learned in transitioning to AI in the medical imaging of COVID-19.” Journal of Medical Imaging 8(S1) (2021): 010902.Link;
4. Fuhrman JD, Gorre N, Hu Q, Li H, El Naqa I, Giger ML. “A review of explainable and interpretable AI with applications in COVID-19 imaging.” Medical Physics (2021).Link;
5. Hu Q, Whitney HM, Li H, Yu J, Liu P, Giger ML. “Improved classification of benign and malignant breast lesions using deep feature maximum intensity projection MRI in breast cancer diagnosis using dynamic contrast-enhance MRI.” Radiology: Artificial Intelligence (2021): p.e200159. Link; In the news
6. Hu Q, Whitney HM, Giger ML. “A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.” Scientific Reports 10.1 (2020): 1-11. Link; In the news
7. Hu Q, Whitney HM, Giger ML. “Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging.” Journal of Medical Imaging 7.4 (2020): 044502. Link
8. Gong H, Hu Q , Walther A, Koo CW, Takahashi EA, Levin DL, Johnson TF, Hora MJ, Leng S, Fletcher JG, McCollough CH. “Deep-learning-based model observer for a lung nodule detection task in computed tomography.” Journal of Medical Imaging 7.4 (2020): 042807. Link

Proceeding papers and extended Abstract

1. Hu Q, Drukker K, Giger ML. “Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning.” Medical Imaging 2021: Computer-Aided Diagnosis. Vol. 11579. International Society for Optics and Photonics, 2021. Link
2. Bhattacharjee R, Douglas L, Drukker K, Hu Q, Fuhrman J, Sheth D, Giger ML. “Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI.” Medical Imaging 2021: Computer-Aided Diagnosis. Vol. 11579. International Society for Optics and Photonics, 2021. Link
3. Hu Q, Drukker K, Giger ML. “Predicting the Need for Intensive Care for COVID-19 Patients using Deep Learning on Chest Radiography.” The 34th Neural Information Processing Systems Conference Medical Imaging meets NeurIPS Workshop. Link
4. Hu Q, Whitney HM, Giger ML. “Using ResNet feature extraction in computer-aided diagnosis of breast cancer on 927 lesions imaged with multiparametric MRI.” Medical Imaging 2020: Computer-Aided Diagnosis. Vol. 11314. International Society for Optics and Photonics, 2020. Link
5. Hu Q, Whitney HM, Giger ML. “Transfer Learning in 4D for Breast Cancer Diagnosis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging.” arXiv preprint arXiv:1911.03022 (2019). Link
6. Hu Q, Whitney HM, Edwards A, Papaioannou J, Giger ML. “Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer.” Medical Imaging 2019: Computer-Aided Diagnosis. Vol. 10950. International Society for Optics and Photonics, 2019. Link
7. Gong H, Walther A, Hu Q, Koo CW, Takahashi EA, Levin DL, Johnson TF, Hora MJ, Leng S, Fletcher JG, McCollough CH, Yu L. “Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT.” Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment. Vol. 10952. International Society for Optics and Photonics, 2019. Link
8. Yu L, Hu Q, Koo CW, Takahashi EA, Levin DL, Johnson TF, Hora MJ, Dirks S, Chen B, McMillan K, Leng S, Fletcher JG, McCollough CH. “PA virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT.” Medical Imaging 2017: Physics of Medical Imaging.. Vol. 10132. International Society for Optics and Photonics, 2017. Link