使用深度学习的医学图像机器解释

Vidhi Chhatbar, Mihir Gondhalekar, Shruti Pimple, R. Pawar
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引用次数: 0

摘要

我们遇到了不同的生物医学图像。这些图像没有任何描述,很难解释。图像字幕是根据图像中的对象和动作从图像中生成文本描述的过程。随着深度学习技术的进步,我们将建立模型来生成生物医学图像的说明文字。该模型将非常有用,以加快诊断过程中存在的异常图像。该模型将基于一个编码器-解码器框架以及一个注意力模型。编码器将使用深度CNN提取图像特征,解码器将使用变压器生成字幕。标题生成涉及不同的复杂场景,从收集数据集、训练模型、验证模型、创建训练模型来测试图像、检测图像和生成标题开始
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Interpretation of Medical Images Using Deep Learning
We come across different biomedical images. It is difficult to interpret those images as they do not have any description. Image captioning is the process of generating textual description from an image which depends on the object and action in the image. With the advancement in deep learning techniques, we will build models to generate captions for biomedical images. This model will be very useful to accelerate the diagnosis process by telling the abnormalities present in the image. The model will be based on an encoder-decoder framework along with an attention model. The encoder will be using deep CNN to extract image features and the decoder will be using transformers to generate captions. Caption generating involves different complex scenarios starting from collecting the data set, training the model, validating the model, creating trained model to test the image, detecting the image and generating the captions
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