AIMC Topic: Deep Learning

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Automated quantification of lipid contents of Lipomyces starkeyi using deep-learning-based image segmentation.

Bioresource technology
Intracellular lipid droplets (LDs), subcellular organelles playing a role in long-term carbon storage, have immense potential in biofuel and dietary lipid production. Monitoring the state of LDs in living cells is of utmost importance for quick bioma...

Prediction and related genes of cancer distant metastasis based on deep learning.

Computers in biology and medicine
Cancer metastasis is one of the main causes of cancer progression and difficulty in treatment. Genes play a key role in the process of cancer metastasis, as they can influence tumor cell invasiveness, migration ability and fitness. At the same time, ...

A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.

Computers in biology and medicine
Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public he...

Identifying keystone species in microbial communities using deep learning.

Nature ecology & evolution
Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial com...

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.

La Radiologia medica
OBJECTIVES: The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological su...

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture.

International journal of language & communication disorders
BACKGROUND: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early dete...

Automatic caries detection in bitewing radiographs: part I-deep learning.

Clinical oral investigations
OBJECTIVE: The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance.

Simple and Multiplexed Detection of Nucleic Acid Targets Based on Fluorescent Ring Patterns and Deep Learning Analysis.

ACS applied materials & interfaces
Simple diagnostic tests for nucleic acid targets can provide great advantages for applications such as rapid pathogen detection. Here, we developed a membrane assay for multiplexed detection of nucleic acid targets based on the visualization of two-d...

Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation.

Physics in medicine and biology
. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy. Secondly, these are converted in multi-leaf collimator (MLC) a...

Accurate staging of chick embryonic tissues via deep learning of salient features.

Development (Cambridge, England)
Recent work shows that the developmental potential of progenitor cells in the HH10 chick brain changes rapidly, accompanied by subtle changes in morphology. This demands increased temporal resolution for studies of the brain at this stage, necessitat...