AIMC Topic: Deep Learning

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MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Re...

Untangling and segmenting the small intestine in 3D cine-MRI using deep learning.

Medical image analysis
Cine-MRI of the abdomen is a non-invasive imaging technique allowing assessment of small intestinal motility. This is valuable for the evaluation of gastrointestinal disorders. While 2D cine-MRI is increasingly used for this purpose in both clinical ...

CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data.

Ultrasonics
Accurate segmentation of kidney in ultrasound images is a vital procedure in clinical diagnosis and interventional operation. In recent years, deep learning technology has demonstrated promising prospects in medical image analysis. However, due to th...

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.

ACS biomaterials science & engineering
Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino ac...

General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis.

Medical physics
PURPOSE: To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-establi...

High-Throughput Recognition of Tumor Cells Using Label-Free Elemental Characteristics Based on Interpretable Deep Learning.

Analytical chemistry
With cancer seriously hampering the increasing life expectancy of people, developing an instant diagnostic method has become an urgent objective. In this work, we developed a label-free laser-induced breakdown spectroscopy (LIBS) method for high-thro...

A deep learning model to detect novel pore-forming proteins.

Scientific reports
Many pore-forming proteins originating from pathogenic bacteria are toxic against agricultural pests. They are the key ingredients in several pesticidal products for agricultural use, including transgenic crops. There is an urgent need to identify no...

Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network.

Frontiers in public health
Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD i...

Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning.

Computational intelligence and neuroscience
This paper integrates classical design theory, multisource urban data, and deep learning to explore an accurate analytical framework in a new data environment, providing a scientific analysis path for the "where" and "how" of greenways in a high-dens...

Prediction of the axial lens position after cataract surgery using deep learning algorithms and multilinear regression.

Acta ophthalmologica
BACKGROUND: The prediction of anatomical axial intraocular lens position (ALP) is one of the major challenges in cataract surgery. The purpose of this study was to develop and test prediction algorithms for ALP based on deep learning strategies.