mlsanityData to 7 Oct 2026

Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"

arXiv 2105.14538 ↗First posted 30 May 2021cs.CV cs.AI cs.CLcited by 16

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Abstract (arXiv)

Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency. In this work, we propose a new context-driven encoding network to automatically generate medical reports for retinal images. The proposed model is mainly composed of a multi-modal input encoder and a fused-feature decoder. Our experimental results show that our proposed method is capable of effectively leveraging the interactive information between the input image and context, i.e., keywords in our case. The proposed method creates more accurate and meaningful reports for retinal images than baseline models and achieves state-of-the-art performance. This performance is shown in several commonly used metrics for the medical report generation task: BLEU-avg (+16%), CIDEr (+10.2%), and ROUGE (+8.6%).

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 27 of its 36 references that are in the map. First posted on arXiv 30 May 2021.

Builds on

27 of 36 references.

References · 27

  1. 2015VQA: Visual Question Answeringcited by 2,489
  2. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  3. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  4. 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 966
  5. 2014Show and Tell: A Neural Image Caption Generatorcited by 877
  6. 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 820
  7. 2017On the Automatic Generation of Medical Imaging Reportscited by 215
  8. 2020X-Linear Attention Networks for Image Captioningcited by 198
  9. 2016Generating Visual Explanationscited by 156
  10. 2018Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generationcited by 141
  11. 2016Improved Image Captioning via Policy Gradient optimization of SPIDErcited by 112
  12. 2014From Captions to Visual Concepts and Backcited by 109
15 more
  1. 2019Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generationcited by 101
  2. 2015Ask Your Neurons: A Neural-based Approach to Answering Questions about Imagescited by 51
  3. 2020DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanationcited by 43
  4. 2021GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarizationcited by 26
  5. 2020Query-controllable Video Summarizationcited by 24
  6. 2017A Novel Framework for Robustness Analysis of Visual QA Modelscited by 20
  7. 2020Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancementcited by 18
  8. 2017VQABQ: Visual Question Answering by Basic Questionscited by 17
  9. 2017Robustness Analysis of Visual QA Models by Basic Questionscited by 17
  10. 2019Assessing the Robustness of Visual Question Answering Modelscited by 17
  11. 2018A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseasescited by 16
  12. 2021Contextualized Keyword Representations for Multi-modal Retinal Image Captioningcited by 16
  13. 2019Synthesizing New Retinal Symptom Images by Multiple Generative Modelscited by 15
  14. 2021PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classificationcited by 10
  15. 2018TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rayscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 5; 2024: 7; 2025: 1; 2026: 2.

16 papers.

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  1. 2021The Dawn of Quantum Natural Language Processingcited by 60
  2. 2023Query-based Video Summarization with Pseudo Label Supervisioncited by 13
  3. 2024A Novel Evaluation Framework for Image2Text Generationcited by 11
  4. 2023Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questionscited by 9
  5. 2023Causalainer: Causal Explainer for Automatic Video Summarizationcited by 8
  6. 2023Conditional Modeling Based Automatic Video Summarizationcited by 6
  7. 2024A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Datacited by 6
  8. 2024Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Modelscited by 5
  9. 2023Causal Video Summarizer for Video Explorationcited by 4
  10. 2024M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generationcited by 3
  11. 2024Automated Retinal Image Analysis and Medical Report Generation through Deep Learningcited by 1
  12. 2024Personalized Video Summarization using Text-Based Queries and Conditional Modelingcited by 1
4 more
  1. 2024GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioningcited by 1
  2. 2025DeepEyeNet: Generating Medical Report for Retinal Imagescited by 1
  3. 2026DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generationcited by 0
  4. 2026AGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classificationcited by 0