Publikationsdatenbank

Learning to be EXACT:
cell detection for asthma on partially annotated whole slide images (2021)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Marzahl, Christian

Bertram, Christof A. (WE 12)

Wilm, Frauke

Voigt, Jörn

Barton, Ann K. (WE 17)

Klopfleisch, Robert (WE 12)

Breininger, Katharina

Maier, Andreas

Aubreville, Marc

Quelle

Arxiv

Bandzählung: 2021

Seiten: Artikel 2101.04943

Sprache

Englisch

Verweise

URL (Volltext): https://arxiv.org/abs/2101.04943

Kontakt

Pferdeklinik

Oertzenweg 19 b
14163 Berlin
+49 30 838 62299 / 62300
pferdeklinik@vetmed.fu-berlin.de

Abstract / Zusammenfassung

Asthma is a chronic inflammatory disorder of the lower respiratory tract and naturally occurs in humans and animals including horses. The annotation of an asthma microscopy whole slide image (WSI) is an extremely labour-intensive task due to the hundreds of thousands of cells per WSI. To overcome the limitation of annotating WSI incompletely, we developed a training pipeline which can train a deep learning-based object detection model with partially annotated WSIs and compensate class imbalances on the fly. With this approach we can freely sample from annotated WSIs areas and are not restricted to fully annotated extracted sub-images of the WSI as with classical approaches. We evaluated our pipeline in a cross-validation setup with a fixed training set using a dataset of six equine WSIs of which four are partially annotated and used for training, and two fully annotated WSI are used for validation and testing. Our WSI-based training approach outperformed classical sub-image-based training methods by up to 15\% mAP and yielded human-like performance when compared to the annotations of ten trained pathologists.