Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging
This work addresses the challenge of accurate medical diagnostics with scarce data in SPECT imaging, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of limited labeled data in medical imaging by adapting Prototypical Networks for few-shot classification and PRNet for anatomical localization in SPECT images, achieving 93.33% validation accuracy for tissue classification and a training loss of 1.395 for localization.
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.