Validation and Correlation with Clinical Data of a Newly Developed Computer Aided Diagnostic System for the Classification of Paranasal Anomalies in the Maxillary Sinus from MRI Images

Benjamin Becker,Debayan Bhattacharya,Christian Betz,Anna Sophie Hoffmann

95th Annual Meeting German Society of Oto-Rhino-Laryngology, Head and Neck Surgery e V, Bonn Laryngo-Rhino-Otologie（2024）

Universitätsklinikum Hamburg-Eppendorf | Technische Universität Hamburg

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Abstract

Introduction Large scale population studies are used to analyse the rate of finding sinus opacities in cranial MRIs (cMRI). Artificial Intelligence support systems can automate the detection of sinus opacities and reduce the workload of clinicians. We developed and evaluated a Computer Aided Diagnostics system based on a 3D Convolutional Neural Network (3D CNN) that automatically extracts and classifies maxillary sinus (MS) from cMRI.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
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【要点】：本文介绍了一种新型计算机辅助诊断系统，该系统基于三维卷积神经网络，用于从MRI影像中自动提取和分类上颌窦异常，并与临床数据进行了验证和相关性分析。

【方法】：研究采用3D卷积神经网络（3D CNN）技术来自动化地处理和分类MRI影像中的上颌窦异常。

【实验】：实验使用了 cranial MRIs 数据集，通过新型计算机辅助诊断系统进行分类，并与临床数据进行了对比验证，结果显示系统具有较高的分类准确性和临床相关性。