AIMC Topic: Deep Learning

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Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images.

The British journal of radiology
OBJECTIVE: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is co...

Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge.

Sensors (Basel, Switzerland)
Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that compe...

Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning.

Nature communications
An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there ar...

Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning.

Physics in medicine and biology
Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automatic algorithms, but there is a lack of research on automatic detection of small HCCs (sHCCs). This st...

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.

Journal of biomolecular structure & dynamics
A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed. To do this, the following studies were carried out: (i) an autoencoder ...

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

Medical image analysis
The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly us...

A deep learning based multiscale approach to segment the areas of interest in whole slide images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). We propose a multi-scale image processing method based on an automatic end-to-end deep neural network algorithm for the segmentation of cancerous areas. A sev...

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network.

Computers in biology and medicine
At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis ar...

CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR.

Computers in biology and medicine
To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intellige...

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Nature biomedical engineering
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 variants) expressed in mammalian cells, which typically results in fe...