AIMC Topic: Image Processing, Computer-Assisted

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Prospective identification of hematopoietic lineage choice by deep learning.

Nature methods
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoi...

Interactive Exploration for Continuously Expanding Neuron Databases.

Methods (San Diego, Calif.)
This paper proposes a novel framework to help biologists explore and analyze neurons based on retrieval of data from neuron morphological databases. In recent years, the continuously expanding neuron databases provide a rich source of information to ...

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

Radiological physics and technology
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I descr...

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features.

Computational intelligence and neuroscience
As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CN...

Extreme learning machine based optimal embedding location finder for image steganography.

PloS one
In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location wi...

Structured Pyramidal Neural Networks.

International journal of neural systems
The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper prop...

Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach.

Sensory tractography and robot-quantified proprioception in hemiparetic children with perinatal stroke.

Human brain mapping
Perinatal stroke causes most hemiparetic cerebral palsy, resulting in lifelong disability. We have demonstrated the ability of robots to quantify sensory dysfunction in hemiparetic children but the relationship between such deficits and sensory tract...

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

NeuroImage. Clinical
Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. ...