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Current Computer-Aided Diagnosis (CAD) methods mainly depend on medical images. The clinical information, which usually needs to be considered in practical clinical diagnosis, has not been fully employed in CAD. In this paper, we propose a novel deep learning-based method for fusing Magnetic Resonance Imaging (MRI)/Computed Tomography (CT) images and clinical information for diagnostic tasks. Two paths of neural layers are performed to extract image features and clinical features, respectively, and at the same time clinical features are employed as the attention to guide the extraction of image features. Finally, these two modalities of features are concatenated to make decisions. We evaluate the proposed method on its applications to Alzheimers disease diagnosis, mild cognitive impairment converter prediction and hepatic microvascular invasion diagnosis. The encouraging experimental results prove the values of the image feature extraction guided by clinical features and the concatenation of two modalities of features for classification, which improve the performance of diagnosis effectively and stably.
Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of
The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVI
A supervised diagnosis system for digital mammogram is developed. The diagnosis processes are done by transforming the data of the images into a feature vector using wavelets multilevel decomposition. This vector is used as the feature tailored towar
Objective: We develop a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the co
We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our segmentation method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow. With our method, we address the