Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging

TL;DR

This paper demonstrates the effectiveness of adapted Prototypical Networks and PRNet for tissue classification and localization in SPECT images with limited labeled data, achieving high accuracy and precise reconstructions.

Contribution

It introduces novel adaptations of Prototypical Networks and PRNet for few-shot classification and localization in SPECT imaging, addressing data scarcity challenges.

Findings

Prototypical Network achieved 96.67% training accuracy.

PRNet accurately reconstructed tissue patches.

The methods show promise for improved deep learning in medical imaging.

Abstract

Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Propagation-Reconstruction Network (PRNet) for few-shot classification and localization, respectively, in Single Photon Emission Computed Tomography (SPECT) images. For the proof of concept we used a 2D-sliced image cropped around heart. The Prototypical Network, with a pre-trained ResNet-18 backbone, classified ventricles, myocardium, and liver tissues with 96.67% training and 93.33% validation accuracy. PRNet, adapted for 2D imaging with an encoder-decoder architecture and skip connections, achieved a training loss of 1.395, accurately reconstructing patches and capturing spatial relationships. These results highlight the potential of Prototypical Networks for tissue classification with limited labeled data and PRNet for anatomical landmark localization, paving the way for improved performance in deep learning frameworks.