AIMC Topic: Deep Learning

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mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology.

mSphere
Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers "Holographic Deep Learning for Rapid Optical Sc...

Gastroenterologist-Level Identification of Small-Bowel DiseasesĀ and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Gastroenterology
BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNN...

Deep learning for the classification of human sperm.

Computers in biology and medicine
BACKGROUND: Infertility is a global health concern, and couples are increasingly seeking medical assistance to achieve reproduction. Semen analysis is a primary assessment performed by a clinician, in which the morphology of the sperm population is e...

Deep learning approach to peripheral leukocyte recognition.

PloS one
Microscopic examination of peripheral blood plays an important role in the field of diagnosis and control of major diseases. Peripheral leukocyte recognition by manual requires medical technicians to observe blood smears through light microscopy, usi...

Anatomical context improves deep learning on the brain age estimation task.

Magnetic resonance imaging
Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network desig...

Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.

PloS one
BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. However, these CNNs failed to outperform the International Symposiu...