Publications of Gabriel Humpire Mamani

2024

PhD theses

  1. G. Humpire-Mamani, "Deep Learning for Localization and Segmentation in Thorax Abdomen CT", PhD thesis, 2024.
    Abstract Url

2023

Preprints

  1. G. Humpire-Mamani, C. Jacobs, M. Prokop, B. van Ginneken and N. Lessmann, "Transfer learning from a sparsely annotated dataset of 3D medical images", arXiv:2311.05032, 2023.
    Abstract DOI arXiv
  2. G. Mamani, N. Lessmann, E. Scholten, M. Prokop, C. Jacobs and B. van Ginneken, "Kidney abnormality segmentation in thorax-abdomen CT scans", arXiv:2309.03383, 2023.
    Abstract DOI arXiv

2022

Papers in international journals

  1. P. Bilic, P. Christ, H. Li, E. Vorontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G. Mamani, G. Chartrand, F. Lohofer, J. Holch, W. Sommer, F. Hofmann, A. Hostettler, N. Lev-Cohain, M. Drozdzal, M. Amitai, R. Vivanti, J. Sosna, I. Ezhov, A. Sekuboyina, F. Navarro, F. Kofler, J. Paetzold, S. Shit, X. Hu, J. Lipkova, M. Rempfler, M. Piraud, J. Kirschke, B. Wiestler, Z. Zhang, C. Hulsemeyer, M. Beetz, F. Ettlinger, M. Antonelli, W. Bae, M. Bellver, L. Bi, H. Chen, G. Chlebus, E. Dam, Q. Dou, C. Fu, B. Georgescu, X. Giro-i-Nieto, F. Gruen, X. Han, P. Heng, J. Hesser, J. Moltz, C. Igel, F. Isensee, P. Jager, F. Jia, K. Kaluva, M. Khened, I. Kim, J. Kim, S. Kim, S. Kohl, T. Konopczynski, A. Kori, G. Krishnamurthi, F. Li, H. Li, J. Li, X. Li, J. Lowengrub, J. Ma, K. Maier-Hein, K. Maninis, H. Meine, D. Merhof, A. Pai, M. Perslev, J. Petersen, J. Pont-Tuset, J. Qi, X. Qi, O. Rippel, K. Roth, I. Sarasua, A. Schenk, Z. Shen, J. Torres, C. Wachinger, C. Wang, L. Weninger, J. Wu, D. Xu, X. Yang, S. Yu, Y. Yuan, M. Yue, L. Zhang, J. Cardoso, S. Bakas, R. Braren, V. Heinemann, C. Pal, A. Tang, S. Kadoury, L. Soler, B. van Ginneken, H. Greenspan, L. Joskowicz and B. Menze, "The Liver Tumor Segmentation Benchmark (LiTS)", Medical Image Analysis, 2022;84:102680.
    Abstract DOI PMID Cited by ~627

2020

Papers in international journals

  1. G. Humpire Mamani, J. Bukala, E. Scholten, M. Prokop, B. van Ginneken and C. Jacobs, "Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning", Radiology: Artificial Intelligence, 2020;2(4):e190102.
    Abstract DOI PMID Algorithm Cited by ~23

2019

Preprints

  1. P. Bilic, P. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C. Fu, X. Han, P. Heng, J. Hesser, S. Kadoury, T. Konopczynski, M. Le, C. Li, X. Li, J. Lipkova, J. Lowengrub, H. Meine, J. Moltz, C. Pal, M. Piraud, X. Qi, J. Qi, M. Rempfler, K. Roth, A. Schenk, A. Sekuboyina, E. Vorontsov, P. Zhou, C. Hulsemeyer, M. Beetz, F. Ettlinger, F. Gruen, G. Kaissis, F. Lohofer, R. Braren, J. Holch, F. Hofmann, W. Sommer, V. Heinemann, C. Jacobs, G. Humpire Mamani, B. van Ginneken, G. Chartrand, A. Tang, M. Drozdzal, A. Ben-Cohen, E. Klang, M. Amitai, E. Konen, H. Greenspan, J. Moreau, A. Hostettler, L. Soler, R. Vivanti, A. Szeskin, N. Lev-Cohain, J. Sosna, L. Joskowicz and B. Menze, "The Liver Tumor Segmentation Benchmark (LiTS)", arXiv:1901.04056, 2019.
    Abstract arXiv Cited by ~627

Abstracts

  1. G. Chlebus, G. Humpire Mamani, A. Schenk, B. van Ginneken and H. Meine, "Mimicking radiologists to improve the robustness of deep-learning based automatic liver segmentation", Annual Meeting of the Radiological Society of North America, 2019.
    Abstract

2018

Papers in international journals

  1. G. Humpire Mamani, A. Setio, B. van Ginneken and C. Jacobs, "Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans", Physics in Medicine and Biology, 2018;63(8):085003.
    Abstract DOI PMID Cited by ~31

2017

Papers in conference proceedings

  1. G. Humpire Mamani, A. Setio, B. van Ginneken and C. Jacobs, "Organ detection in thorax abdomen CT using multi-label convolutional neural networks", Medical Imaging, 2017;10134.
    Abstract DOI Cited by ~16

Abstracts

  1. J. Bukala, G. Humpire Mamani, E. Scholten, M. Prokop, B. van Ginneken and C. Jacobs, "Fully Automatic Measurement of the Splenic Volume in CT with U-Net Convolutional Neural Networks", Annual Meeting of the Radiological Society of North America, 2017.
    Abstract