AIMC Topic: Deep Learning

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ALLD: Acute Lymphoblastic Leukemia Detector.

Studies in health technology and informatics
Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we d...

Categorical Perception: A Groundwork for Deep Learning.

Neural computation
Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical perception, i...

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with com...

HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods...

scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thu...

Gold-viral particle identification by deep learning in wide-field photon scattering parametric images.

Applied optics
The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution ins...

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Molecular biology and evolution
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method t...

FP-nets as novel deep networks inspired by vision.

Journal of vision
Feature-product networks (FP-nets) are inspired by end-stopped cortical cells with FP-units that multiply the outputs of two filters. We enhance state-of-the-art deep networks, such as the ResNet and MobileNet, with FP-units and show that the resulti...

Inverting the structure-property map of truss metamaterials by deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady...

Diagnosis of Choroidal Disease With Deep Learning-Based Image Enhancement and Volumetric Quantification of Optical Coherence Tomography.

Translational vision science & technology
PURPOSE: The purpose of this study was to quantify choroidal vessels (CVs) in pathological eyes in three dimensions (3D) using optical coherence tomography (OCT) and a deep-learning analysis.