杠杆(统计)
计算机科学
人工智能
深度学习
医学影像学
图像分割
特征学习
机器学习
分割
深层神经网络
图像(数学)
模式识别(心理学)
作者
Shih-Cheng Huang,Liyue Shen,Matthew P. Lungren,Serena Yeung
标识
DOI:10.1109/iccv48922.2021.00391
摘要
In recent years, the growing utilization of medical imaging is placing an increasing burden on radiologists. Deep learning provides a promising solution for automatic medical image analysis and clinical decision support. However, large-scale manually labeled datasets required for training deep neural networks are difficult and expensive to obtain for medical images. The purpose of this work is to develop label-efficient multimodal medical imaging representations by leveraging radiology reports. We propose an attention-based framework for learning global and local representations by contrasting image sub-regions and words in the paired report. In addition, we propose methods to leverage the learned representations for various downstream medical image recognition tasks with limited labels. Our results demonstrate high-performance and label-efficiency for image-text retrieval, classification (finetuning and zerosshot settings), and segmentation on different datasets.
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