TUPAC16_AL

A set of alternative labels for the TUPAC16 challenge auxiliary mitosis dataset. Our research paper shows that it's more complete and has a lower level of label noise.

A set of alternative labels for the TUPAC16 challenge auxiliary mitosis dataset. Our research paper shows that it's more complete and has a lower level of label noise.

Resources

Publications

  • Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels

    Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi, Christof A. Bertram, Mitko Veta, Christian Marzahl, Nikolas Stathonikos, Andreas Maier, Robert Klopfleisch and Marc Aubreville

    2020 · Interpretable and Annotation-Efficient Learning for Medical Image Computing doi link

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