Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography von Tachibana, Rie, Näppi, Janne J, Ota, Junko, Kohlhase, Nadja, Hironaka, Toru, Kim, Se Hyung, Regge, Daniele, Yoshida, Hiroyuki

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AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging von Hadjiiski, Lubomir, Cha, Kenny, Chan, Heang‐Ping, Drukker, Karen, Morra, Lia, Näppi, Janne J., Sahiner, Berkman, Yoshida, Hiroyuki, Chen, Quan, Deserno, Thomas M., Greenspan, Hayit, Huisman, Henkjan, Huo, Zhimin, Mazurchuk, Richard, Petrick, Nicholas, Regge, Daniele, Samala, Ravi, Summers, Ronald M., Suzuki, Kenji, Tourassi, Georgia, Vergara, Daniel, Armato, Samuel G.

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Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT von Uemura, Tomoki, Näppi, Janne J., Watari, Chinatsu, Hironaka, Toru, Kamiya, Tohru, Yoshida, Hiroyuki

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U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19 von Näppi, Janne J., Uemura, Tomoki, Watari, Chinatsu, Hironaka, Toru, Kamiya, Tohru, Yoshida, Hiroyuki

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Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study von Tachibana, Rie, Näppi, Janne J, Hironaka, Toru, Yoshida, Hiroyuki

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A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography von Uemura, Tomoki, Näppi, Janne J., Ryu, Yasuji, Watari, Chinatsu, Kamiya, Tohru, Yoshida, Hiroyuki

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Bayesian uncertainty estimation for detection of long-tailed and unseen conditions in medical images von Rezaei, Mina, Näppi, Janne J., Bischl, Bernd, Yoshida, Hiroyuki

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Generative multi-adversarial network for striking the right balance in abdominal image segmentation von Rezaei, Mina, Näppi, Janne J., Lippert, Christoph, Meinel, Christoph, Yoshida, Hiroyuki

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Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps von Yoshida, H., Nappi, J.

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Centerline-based colon segmentation for CT colonography von Frimmel, Hans, Näppi, J., Yoshida, H.

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Abdominal Imaging. Computational and Clinical Applications: 6th International Workshop, ABDI 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 14, 2014 von Yoshida, Hiroyuki, Näppi, Janne J, Saini, Sanjay

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Computerized detection of colorectal masses in CT colonography based on fuzzy merging and wall-thickening analysis von Näppi, Janne J., Frimmel, Hans, Dachman, Abraham H., Yoshida, Hiroyuki

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Accurate segmentation of the breast region from digitized mammograms von Ojala, T., Näppi, J., Nevalainen, O.

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Melanogenesis and associated cytotoxic reactions: Applications to insect innate immunity von Nappi, A.J., Christensen, B.M.

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Transfer-learning deep convolutional neural network for classification of polyp candidates on CT colonography von UEMURA, Tomoki, LU, Huimin, KIM, Hyoungseop, TACHIBANA, Rie, HIRONAKA, Toru, J., Näppi Janne, YOSHIDA, Hiroyuki

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T helper 1 effector memory CD4+ T cells protect the skin from poxvirus infection von Harbour, Jake C., Abdelbary, Mahmoud, Schell, John B., Fancher, Samantha P., McLean, Jack J., Nappi, Taylen J., Liu, Susan, Nice, Timothy J., Xia, Zheng, Früh, Klaus, Nolz, Jeffrey C.

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Melanization immune responses in mosquito vectors von Christensen, Bruce M., Li, Jianyong, Chen, Cheng-Chen, Nappi, Anthony J.

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Insect immune resistance to parasitoids von Carton, Yves, Poirié, Marylène, Nappi, Anthony J

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CADe Prompts and Observer Performance von Näppi, Janne J., PhD

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Adaptive correction of the pseudo-enhancement of CT attenuation for fecal-tagging CT colonography von Näppi, Janne, Yoshida, Hiroyuki

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