Naslov
Self-supervised Learning as a Means to Reduce the Need for Labeled Data in Medical Image Analysis

Autori
Benčević, Marin ; Habijan, Marija ; Galić, Irena ; Pizurica, Aleksandra

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2022 30th European Signal Processing Conference (EUSIPCO) / - : Institute of Electrical and Electronics Engineers (IEEE), 2022, 1328-1332

Skup
30th European Signal Processing Conference (EUSIPCO)

Mjesto i datum
Beograd, Srbija, 29.08.2022. - 02.09.2022

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
contrastive learning ; deep learning ; medical image processing ; object detection ; self-supervised learning

Sažetak
One of the largest problems in medical image processing is the lack of annotated data. Labeling medical images often requires highly trained experts and can be a time-consuming process. In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining. We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. The networks are pretrained on a percentage of the dataset without labels and then fine-tuned on the rest of the dataset. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60\% of the labeled data. We also show that it is possible to increase the maximum performance of a fully-supervised model by adding a self- supervised pretraining step, and this effect can be observed with even a small amount of unlabeled data for pretraining.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA