Representation Learning in PET Scans Enhanced by Semantic and 3D Position Specific Characteristics

Representation learning methods that discover task and/or data-specific characteristics are very popular for a variety of applications. However, their application to 3D medical images is restricted by the computational cost and their inherent subtle differences in intensities and appearance. In this paper, a novel representation learning scheme for extracting representations capable of distinguishing high-uptake regions from 3D 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) images is proposed. In particular, we propose a novel position-enhanced learning scheme effectively incorporating semantic and position-based features through our proposed Position Encoding Block (PEB) to produce highly informative representations. Such representations incorporate both semantic and position-aware features from high-dimensional medical data, leading to general representations with better performance on clinical tasks. To evaluate our method, we conducted experiments on the challenging task of classifying high-uptake regions as either non-tumor or tumor lesions in Metastatic Melanoma (MM). MM is a type of cancer characterized by its rapid spread to various body sites, which leads to low survival rates. Extensive experiments on an in-house and a public dataset of whole-body FDG-PET images indicated an increase of 10.50% in sensitivity and 4.89% in F1-score against the baseline representation learning scheme while also outperforming state-of-the-art methods for classifying MM regions of interest. The source code will be available at https://github.com/theoVag/Representation-Learning-Sem-Pos.