AIMC Topic: Deep Learning

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A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells.

Computational and mathematical methods in medicine
Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains unde...

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

The American journal of pathology
With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from...

A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans.

Medical image analysis
The objective quantification of retinal atrophy associated with age-related macular degeneration (AMD) is required for clinical diagnosis, follow-up, treatment efficacy evaluation, and clinical research. Spectral Domain Optical Coherence Tomography (...

Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Cancer is a serious global disease due to its high mortality, and the key to effective treatment is accurate diagnosis. However, limited by sampling difficulty and actual sample size in clinical practice, data imbalance is a...

Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan.

Computers in biology and medicine
This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchm...

Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks.

Sensors (Basel, Switzerland)
The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper b...

A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations.

Genes
Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools have been developed to predict the free energy changes upon protein residue variations. Most of these...

Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.

European journal of radiology
OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outcome. It is unknown if deep learning automated detection of PE on CT Pulmonary Angiograms (CTPA) in co...

Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.

Computers in biology and medicine
Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quant...

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

European journal of radiology
OBJECTIVE: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm.