3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI
This work addresses the challenge of limited labeled data in medical imaging for prostate cancer diagnosis, though it is incremental as it adapts an existing method to a specific domain.
The study tackled the problem of classifying prostate cancer lesions in 3D MRI data by applying Masked Image Modelling (MIM) as a self-supervised pre-training method, achieving better AUC results than ImageNet weight initialization.
Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce and there is still uncertainty. around the potential of such a learning paradigm in the medical domain. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data. In particular, we explore the effect of using MIM when coupled with convolutional neural networks (CNNs) under different conditions such as different masking strategies, obtaining better results in terms of AUC than other pre-training strategies like ImageNet weight initialization.