Reducing the Effect of Incomplete Annotations in Object Detection for Histopathology

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dc.contributor.advisorPapkov, Mikhail, juhendaja
dc.contributor.advisorFishman, Dmytro, juhendaja
dc.contributor.authorKaliuzhnyi, Denys
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2023-11-02T10:42:20Z
dc.date.available2023-11-02T10:42:20Z
dc.date.issued2023
dc.description.abstractHistopathology is a crucial component of clinical practice involving microscopic tissue examination. Typically, pathologists manually analyse tissue to locate and label structural units, cells, and organoids. The properties and quantity of these objects can indicate a patient’s condition, e.g., the presence of tumours. Recent advancements in artificial intelligence (AI) have created the potential to automate this process. However, AI methods either provide limited accuracy or require a lot of densely annotated data, which is prohibitively time-consuming and expensive in the histopathology domain due to high object density and labelling difficulty. In this study, we address the challenge of training object detection neural networks on histology data with incomplete annotations. We demonstrate that hyperparameter tuning can mitigate the negative effects of sparsely labelled data. Additionally, we propose a novel model component called the Generalised Background Recalibration Loss to further improve detection rates. It can be adapted to a broader class of object detection models than previous solutions. Our results should facilitate the development of object detection neural networks for histology images by demonstrating the efficient use of sparsely labelled data. Our method reduces the impact of missing annotations on detection rates and thereby eases the most time-consuming aspect of data preparation for neural network training.et
dc.identifier.urihttps://hdl.handle.net/10062/93946
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectdeep learninget
dc.subjectcomputer visionet
dc.subjectneural networkset
dc.subjectobject detectionet
dc.subjectsparsely annotated objectset
dc.subjecttraining under incomplete annotationset
dc.subjecthistopathologyet
dc.subjectmicroscopy imaginget
dc.subject.othermagistritöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticset
dc.subject.otherinfotechnologyet
dc.titleReducing the Effect of Incomplete Annotations in Object Detection for Histopathologyet
dc.typeThesiset

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