Summary of Vision-language Models For Medical Report Generation and Visual Question Answering: a Review, by Iryna Hartsock and Ghulam Rasool
Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review
by Iryna Hartsock, Ghulam Rasool
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medical vision-language models (VLMs) combine computer vision and natural language processing to analyze visual and textual medical data. This paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on models designed for medical report generation and visual question answering. It provides background on NLP and CV, explaining how techniques from both fields are integrated into VLMs to enable learning from multimodal data. The review explores medical vision-language datasets, analyzes architectures and pre-training strategies in recent noteworthy medical VLMs, and discusses evaluation metrics for assessing VLMs’ performance in medical report generation and visual question answering. It also highlights current challenges and proposes future directions, including enhancing clinical validity and addressing patient privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical vision-language models (VLMs) help doctors use pictures and words to understand patients better. This paper looks at how these models are being used in healthcare. It explains what computer vision and natural language processing are, and how they work together in VLMs. The paper also talks about special datasets for medical VLMs, the kinds of models that have been developed, and how well they do on tests. Finally, it mentions some challenges and ideas for making these models even better. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Question answering
