Summary of Multi-oct-selfnet: Integrating Self-supervised Learning with Multi-source Data Fusion For Enhanced Multi-class Retinal Disease Classification, by Fatema-e- Jannat et al.
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification
by Fatema-E- Jannat, Sina Gholami, Jennifer I. Lim, Theodore Leng, Minhaj Nur Alam, Hamed Tabkhi
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel approach to developing a deep-learning model for retinal disease diagnosis, overcoming the challenges posed by limited datasets in the medical domain. The authors combine multiple data sources to improve performance and generalize effectively on smaller datasets. They develop a self-supervised framework based on large language models (LLMs), SwinV2, to gain a deeper understanding of multi-modal dataset representations. This enables the model to extrapolate to new data for detecting eye diseases using optical coherence tomography (OCT) images. The study adopts a two-phase training methodology, involving self-supervised pre-training and fine-tuning on a downstream supervised classifier. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simpler terms, this paper solves a big problem in medical AI by combining many different datasets to train a deep-learning model that can accurately diagnose retinal diseases using OCT images. They develop a new framework that allows the model to learn from diverse data sources and generalize well to new data. This breakthrough could lead to more accurate diagnoses and better healthcare outcomes. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Multi modal » Self supervised » Supervised
