Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists

Author:

Komura Daisuke,Onoyama Takumi,Shinbo Koki,Odaka Hiroto,Hayakawa Minako,Ochi Mieko,Herdiantoputri Ranny Rahaningrum,Endo Haruya,Katoh Hiroto,Ikeda Tohru,Ushiku Tetsuo,Ishikawa ShumpeiORCID

Publisher

Elsevier BV

Subject

General Decision Sciences

Reference49 articles.

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2. PanCancer insights from The Cancer Genome Atlas: the pathologist’s perspective;Cooper;J. Pathol.,2018

3. NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images;Lal;Comput. Biol. Med.,2021

4. Nuclei segmentation in histopathology images using deep neural networks;Naylor,2017

5. CoNIC: Colon Nuclei Identification and Counting Challenge 2022;Graham;arXiv,2021

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