AIMC Topic: Diagnostic Imaging

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Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

Radiology
The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires caref...

Multiscale High-Level Feature Fusion for Histopathological Image Classification.

Computational and mathematical methods in medicine
Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It c...

A mixed-scale dense convolutional neural network for image analysis.

Proceedings of the National Academy of Sciences of the United States of America
Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper ...

Computational Intelligence for Medical Imaging Simulations.

Journal of medical systems
This paper describes how to simulate medical imaging by computational intelligence to explore areas that cannot be easily achieved by traditional ways, including genes and proteins simulations related to cancer development and immunity. This paper ha...

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

Biomedical engineering online
BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of...

Classification of Medical Images in the Biomedical Literature by Jointly Using Deep and Handcrafted Visual Features.

IEEE journal of biomedical and health informatics
The classification of medical images and illustrations from the biomedical literature is important for automated literature review, retrieval, and mining. Although deep learning is effective for large-scale image classification, it may not be the opt...

Radiomics and radiogenomics in lung cancer: A review for the clinician.

Lung cancer (Amsterdam, Netherlands)
Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening o...

Machine Learning for Predicting Patient Wait Times and Appointment Delays.

Journal of the American College of Radiology : JACR
Being able to accurately predict waiting times and scheduled appointment delays can increase patient satisfaction and enable staff members to more accurately assess and respond to patient flow. In this work, the authors studied the applicability of m...

Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens o...

Sensor, Signal, and Imaging Informatics.

Yearbook of medical informatics
To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensor...