AIMC Topic: Deep Learning

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A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging.

BMC medical informatics and decision making
BACKGROUND: Computed tomography (CT) reports record a large volume of valuable information about patients' conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic s...

A geometry-guided deep learning technique for CBCT reconstruction.

Physics in medicine and biology
Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL techn...

Deep learning with robustness to missing data: A novel approach to the detection of COVID-19.

PloS one
In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around the world differ enormously in what laborat...

CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction.

European radiology
OBJECTIVES: The aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and...

CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning.

Neural networks : the official journal of the International Neural Network Society
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs ...

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

Journal of infection and public health
BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the necessary lifesaving facilities like ICU care and ventilator support. For this purpose, we designed a computer-aided diagnosis and severity detection meth...

TEM virus images: Benchmark dataset and deep learning classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: To achieve the full potential of deep learning (DL) models, such as understanding the interplay between model (size), training strategy, and amount of training data, researchers and developers need access to new dedicated im...

Impact of transfer learning for human sperm segmentation using deep learning.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Infertility affects approximately one in ten couples, and almost half of the infertility cases are due to the malefactor. To diagnose infertility and determine future treatment, a semen analysis is performed. Evaluation of s...

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images.

Computers in biology and medicine
Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these netwo...

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Journal of digital imaging
Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical under...