Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging
Journal:
arXiv
Published Date:
Feb 10, 2025
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.
