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Diagnosis, Computer-Assisted

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A scoping review on multimodal deep learning in biomedical images and texts.

Journal of biomedical informatics
OBJECTIVE: Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images an...

[Artificial Intelligence for computer-aided leukemia diagnostics].

Deutsche medizinische Wochenschrift (1946)
The manual examination of blood and bone marrow specimens for leukemia patients is time-consuming and limited by intra- and inter-observer variance. The development of AI algorithms for leukemia diagnostics requires high-quality sample digitization a...

OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks.

International journal of computer assisted radiology and surgery
PURPOSE: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional n...

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

Medical image analysis
Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular struc...

A novel collaborative self-supervised learning method for radiomic data.

NeuroImage
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this...

A multi-modal deep neural network for multi-class liver cancer diagnosis.

Neural networks : the official journal of the International Neural Network Society
Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This study proposes a multi-modal deep neural network for multi-class malignant liver diagnosis. In parallel with the portal venous computed tomography (C...

A survey on deep learning for skin lesion segmentation.

Medical image analysis
Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of ...

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.

IEEE transactions on medical imaging
In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnos...

Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver ...

A state-of-the-art survey of artificial neural networks for Whole-slide Image analysis: From popular Convolutional Neural Networks to potential visual transformers.

Computers in biology and medicine
In recent years, with the advancement of computer-aided diagnosis (CAD) technology and whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the diagnosis and analysis of diseases. To increase the objectivity and acc...