Summary of Uterine Ultrasound Image Captioning Using Deep Learning Techniques, by Abdennour Boulesnane et al.
Uterine Ultrasound Image Captioning Using Deep Learning Techniques
by Abdennour Boulesnane, Boutheina Mokhtari, Oumnia Rana Segueni, Slimane Segueni
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the application of deep learning for medical image captioning, focusing on uterine ultrasound images crucial for diagnosing and monitoring obstetric and gynecological conditions. A hybrid model combining Convolutional Neural Networks (CNNs) with Bidirectional Gated Recurrent Units (BGRUs) is developed to generate descriptive captions. Experimental results show the proposed approach outperforms baseline methods in terms of BLEU and ROUGE scores, demonstrating its effectiveness in generating accurate captions. This research aims to enhance medical professionals’ interpretation skills, leading to improved patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical imaging has come a long way since X-rays were first used. Now, we have advanced technologies like MRIs and CT scans that help doctors diagnose and treat patients. But even with these tools, it can be hard for doctors to understand some medical images, especially ultrasound pictures of the uterus. These images are important for diagnosing and monitoring women’s health, but they can be tricky to interpret. To make it easier, researchers developed a new way to use artificial intelligence (AI) to describe these ultrasound images. This AI system uses two types of neural networks: one that looks at the image and another that looks at words. The system works by combining both approaches to generate captions that accurately describe what’s in the image. In tests, this approach worked better than other methods, showing it can be a useful tool for doctors. |
Keywords
» Artificial intelligence » Bleu » Deep learning » Image captioning » Rouge
