@inproceedings{401d5af1d7e141ad8dbc7404da779edb,
title = "Multi-level activation for segmentation of hierarchically-nested classes",
abstract = "For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results. However, most {\textquoteleft}out-of-the-box{\textquoteright} CNN models are still blind to such prior information. In this paper, we propose a novel approach to encode this information, through a multi-level activation layer and three compatible losses. We benchmark all of them on nuclei segmentation in bright-field microscopy cell images from the 2018 Data Science Bowl challenge, offering an exemplary segmentation task with cells and nested subcellular structures. Our scheme greatly speeds up learning, and outperforms standard multi-class classification with soft-max activation and a previously proposed method stemming from it, improving the Dice score significantly (p-values < 0.007). Our approach is conceptually simple, easy to implement and can be integrated in any CNN architecture. It can be generalized to a higher number of classes, with or without further relations of containment.",
keywords = "Class hierarchy, Inclusion, Multiclass, Nested classes, Segmentation",
author = "Marie Piraud and Anjany Sekuboyina and Menze, {Bj{\"o}rn H.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2019",
doi = "10.1007/978-3-030-11024-6_24",
language = "English",
isbn = "9783030110239",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "345--353",
editor = "Laura Leal-Taix{\'e} and Stefan Roth",
booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",
}