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Disease Progression

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Prediction of chronic kidney disease and its progression by artificial intelligence algorithms.

Journal of nephrology
BACKGROUND AND OBJECTIVE: Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitio...

Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images.

Sensors (Basel, Switzerland)
We propose an encoder-decoder architecture using wide and deep convolutional layers combined with different aggregation modules for the segmentation of medical images. Initially, we obtain a rich representation of features that span from low to high ...

A Novel Transformer-Based Attention Network for Image Dehazing.

Sensors (Basel, Switzerland)
Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and de...

Machine learning predicts cancer subtypes and progression from blood immune signatures.

PloS one
Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapi...

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

European radiology
OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and stagi...

Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.

Journal of digital imaging
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialist...

Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field.

Sensors (Basel, Switzerland)
The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classifica...

Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjusting therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms...

Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developi...