# Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease Classification

arXiv: https://arxiv.org/abs/2412.02825 · HTML: https://mlsanity.com/paper/2412.02825/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images](https://mlsanity.com/paper/2302.02991/) (2023)
- [AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights](https://mlsanity.com/paper/2006.08217/) (2020)
- [Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.07862/) (2024)
- [Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation](https://mlsanity.com/paper/2105.09365/) (2021)
- [RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection](https://mlsanity.com/paper/2407.12271/) (2024)

## Built on by (most cited first, 2 of 2)

- [nnMobileNet++: Towards Efficient Hybrid Networks for Retinal Image Analysis](https://mlsanity.com/paper/2512.01273/) (2025)
- [AtomDiffuser: Time-Aware Degradation Modeling for Drift and Beam Damage in STEM Imaging](https://mlsanity.com/paper/2508.10359/) (2025)

## Abstract (arXiv)

In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training multiple models with distinct data augmentation strategies and different model complexities. Through this fusion technique, we achieved robust generalization in data-scarce domains while balancing computational efficiency with feature extraction capabilities.