AIMC Topic: Animals

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Feasibility of the use of deep learning classification of teat-end condition in Holstein cattle.

Journal of dairy science
Infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows; therefore, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection....

Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis.

Toxicologic pathology
Inflammatory bowel disease (IBD) is a complex disease which leads to life-threatening complications and decreased quality of life. The dextran sulfate sodium (DSS) colitis model in mice is known for rapid screening of candidate compounds. Efficacy as...

Unsupervised manifold learning of collective behavior.

PLoS computational biology
Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generat...

Giant Panda Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The lack of automatic tools to identify giant panda makes it hard to keep track of and manage giant pandas in wildlife conservation missions. In this paper, we introduce a new Giant Panda Identification (GPID) task, which aims to identify each indivi...

Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggl...

Behavioral validation of novel high resolution attention decoding method from multi-units & local field potentials.

NeuroImage
The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field...

Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning.

Nature methods
Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extra...

DeepACSON automated segmentation of white matter in 3D electron microscopy.

Communications biology
Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and r...

Systematic auditing is essential to debiasing machine learning in biology.

Communications biology
Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify ...

Automatic deep learning-driven label-free image-guided patch clamp system.

Nature communications
Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cell...