# Latents of latents to delineate pixels: hybrid Matryoshka autoencoder-to-U-Net pairing for segmenting large medical images in GPU-poor and low-data regimes

arXiv: https://arxiv.org/abs/2502.08988 · HTML: https://mlsanity.com/paper/2502.08988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)

## Abstract (arXiv)

Medical images are often high-resolution and lose important detail if downsampled, making pixel-level methods such as semantic segmentation much less efficient if performed on a low-dimensional image. We propose a low-rank Matryoshka projection and a hybrid segmenting architecture that preserves important information while retaining sufficient pixel geometry for pixel-level tasks. We design the Matryoshka Autoencoder (MatAE-U-Net) which combines the hierarchical encoding of the Matryoshka Autoencoder with the spatial reconstruction capabilities of a U-Net decoder, leveraging multi-scale feature extraction and skip connections to enhance accuracy and generalisation. We apply it to the problem of segmenting the left ventricle (LV) in echocardiographic images using the Stanford EchoNet-D dataset, including 1,000 standardised video-mask pairs of cardiac ultrasound videos resized to 112x112 pixels. The MatAE-UNet model achieves a Mean IoU of 77.68%, Mean Pixel Accuracy of 97.46%, and Dice Coefficient of 86.91%, outperforming the baseline U-Net, which attains a Mean IoU of 74.70%, Mean Pixel Accuracy of 97.31%, and Dice Coefficient of 85.20%. The results highlight the potential of using the U-Net in the recursive Matroshka latent space for imaging problems with low-contrast such as echocardiographic analysis.