Summary of Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning For Advanced Disease Analysis, by Ziyan Yao et al.
Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis
by Ziyan Yao, Fei Lin, Sheng Chai, Weijie He, Lu Dai, Xinghui Fei
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 The innovative multi-modal deep learning model proposed in this paper aims to deeply integrate heterogeneous information from medical images and clinical reports. It combines convolutional neural networks (CNNs) for extracting features from medical images with a two-way long and short-term memory network and attention mechanism for analyzing clinical report text. The integrated features interact through a designed fusion layer, enabling joint representation learning of image and text. Experimental results on a large medical image database show the model’s superiority in disease classification, lesion localization, and clinical description generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use computers to help doctors by combining information from medical images and patient reports. It uses special computer programs called neural networks that can look at pictures and understand what’s written. The program is trained on lots of images and texts and then tested to see how well it works. The results are very good, showing the program can correctly identify diseases, find problems in images, and even write its own reports. |
Keywords
» Artificial intelligence » Attention » Classification » Deep learning » Multi modal » Representation learning
