AIMC Topic: Deep Learning

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A Deep Learning Pipeline for Nucleus Segmentation.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datas...

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

BMC medical informatics and decision making
BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop progno...

Applications of deep learning in dentistry.

Oral surgery, oral medicine, oral pathology and oral radiology
Over the last few years, translational applications of so-called artificial intelligence in the field of medicine have garnered a significant amount of interest. The present article aims to review existing dental literature that has examined deep lea...

Frequency and phase correction of J-difference edited MR spectra using deep learning.

Magnetic resonance in medicine
PURPOSE: To investigate whether a deep learning-based (DL) approach can be used for frequency-and-phase correction (FPC) of MEGA-edited MRS data.

Deep learning in cancer pathology: a new generation of clinical biomarkers.

British journal of cancer
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, bi...

Joint Local and Global Information Learning With Single Apex Frame Detection for Micro-Expression Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Micro-expressions (MEs) are rapid and subtle facial movements that are difficult to detect and recognize. Most recent works have attempted to recognize MEs with spatial and temporal information from video clips. According to psychological studies, th...

Hierarchical Paired Channel Fusion Network for Street Scene Change Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Street Scene Change Detection (SSCD) aims to locate the changed regions between a given street-view image pair captured at different times, which is an important yet challenging task in the computer vision community. The intuitive way to solve the SS...

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition.

BMC bioinformatics
BACKGROUND: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment leve...

Systems biology informed deep learning for inferring parameters and hidden dynamics.

PLoS computational biology
Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algori...