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

arXiv: https://arxiv.org/abs/2502.06632 · HTML: https://mlsanity.com/paper/2502.06632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)

## Built on by (most cited first, 3 of 3)

- [Test-time Prompt Refinement for Text-to-Image Models](https://mlsanity.com/paper/2507.22076/) (2025)
- [NORA: A Nephrology-Oriented Representation Learning Approach Towards Chronic Kidney Disease Classification](https://mlsanity.com/paper/2509.12704/) (2025)
- [LVADNet3D: A Deep Autoencoder for Reconstructing 3D Intraventricular Flow from Sparse Hemodynamic Data](https://mlsanity.com/paper/2509.16860/) (2025)

## Abstract (arXiv)

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.