AIMC Topic: Image Processing, Computer-Assisted

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Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual-energy fluoroscopy.

Medical physics
PURPOSE: To present a novel method, based on convolutional neural networks (CNN), to automate weighted log subtraction (WLS) for dual-energy (DE) fluoroscopy to be used in conjunction with markerless tumor tracking (MTT).

Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging.

BioMed research international
OBJECTIVE: The incidence of superficial organ diseases has increased rapidly in recent years. New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency. Convolutional neural networks (CNNs) are one of the mos...

Biomarker Localization From Deep Learning Regression Networks.

IEEE transactions on medical imaging
Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation ...

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Artificial intelligence in medicine
Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, hea...

Bayesian deep matrix factorization network for multiple images denoising.

Neural networks : the official journal of the International Neural Network Society
This paper aims at proposing a robust and fast low rank matrix factorization model for multiple images denoising. To this end, a novel model, Bayesian deep matrix factorization network (BDMF), is presented, where a deep neural network (DNN) is design...

Crowding reveals fundamental differences in local vs. global processing in humans and machines.

Vision research
Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have sugge...

Evaluating Convolutional Neural Networks for Cage-Free Floor Egg Detection.

Sensors (Basel, Switzerland)
The manual collection of eggs laid on the floor (or 'floor eggs') in cage-free (CF) laying hen housing is strenuous and time-consuming. Using robots for automatic floor egg collection offers a novel solution to reduce labor yet relies on robust egg d...

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually la...

Radon Inversion via Deep Learning.

IEEE transactions on medical imaging
The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly important in disease screening and diagnosis. In this paper, we propose a novel r...

A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional DenseNet.

Medical physics
PURPOSE: Prostate cancer (PCa) is a major health concern in aging males, and proper management of the disease depends on accurately interpreting pathology specimens. However, reading prostatectomy histopathology slides, which is basically for staging...