# Automated Retinal Image Analysis and Medical Report Generation through Deep Learning

arXiv: https://arxiv.org/abs/2408.07349 · HTML: https://mlsanity.com/paper/2408.07349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 150 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation](https://mlsanity.com/paper/1805.08298/) (2018)
- [A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input](https://mlsanity.com/paper/1410.0210/) (2014)
- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://mlsanity.com/paper/2106.09296/) (2021)
- [Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations](https://mlsanity.com/paper/1905.06139/) (2019)
- [VQABQ: Visual Question Answering by Basic Questions](https://mlsanity.com/paper/1703.06492/) (2017)
- [Robustness Analysis of Visual QA Models by Basic Questions](https://mlsanity.com/paper/1709.04625/) (2017)
- [Assessing the Robustness of Visual Question Answering Models](https://mlsanity.com/paper/1912.01452/) (2019)
- [Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics](https://mlsanity.com/paper/2406.15264/) (2024)
- [A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases](https://mlsanity.com/paper/1806.06423/) (2018)
- [Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"](https://mlsanity.com/paper/2105.14538/) (2021)
- [Query-based Video Summarization with Pseudo Label Supervision](https://mlsanity.com/paper/2307.01945/) (2023)
- [A Novel Evaluation Framework for Image2Text Generation](https://mlsanity.com/paper/2408.01723/) (2024)
- [Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions](https://mlsanity.com/paper/2304.03147/) (2023)
- [Conditional Modeling Based Automatic Video Summarization](https://mlsanity.com/paper/2311.12159/) (2023)
- [Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks](https://mlsanity.com/paper/2405.13372/) (2024)

## Built on by (most cited first, 1 of 1)

- [Personalized Video Summarization using Text-Based Queries and Conditional Modeling](https://mlsanity.com/paper/2408.14743/) (2024)

## Abstract (arXiv)

The increasing prevalence of retinal diseases poses a significant challenge to the healthcare system, as the demand for ophthalmologists surpasses the available workforce. This imbalance creates a bottleneck in diagnosis and treatment, potentially delaying critical care. Traditional methods of generating medical reports from retinal images rely on manual interpretation, which is time-consuming and prone to errors, further straining ophthalmologists' limited resources. This thesis investigates the potential of Artificial Intelligence (AI) to automate medical report generation for retinal images. AI can quickly analyze large volumes of image data, identifying subtle patterns essential for accurate diagnosis. By automating this process, AI systems can greatly enhance the efficiency of retinal disease diagnosis, reducing doctors' workloads and enabling them to focus on more complex cases. The proposed AI-based methods address key challenges in automated report generation: (1) Improved methods for medical keyword representation enhance the system's ability to capture nuances in medical terminology; (2) A multi-modal deep learning approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports; (3) Techniques to enhance the interpretability of the AI-based report generation system, fostering trust and acceptance in clinical practice. These methods are rigorously evaluated using various metrics and achieve state-of-the-art performance. This thesis demonstrates AI's potential to revolutionize retinal disease diagnosis by automating medical report generation, ultimately improving clinical efficiency, diagnostic accuracy, and patient care. [https://github.com/Jhhuangkay/DeepOpht-Medical-Report-Generation-for-Retinal-Images-via-Deep-Models-and-Visual-Explanation]