AIMC Topic: Image Processing, Computer-Assisted

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Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known.

Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Person re-identification (Re-ID) aims at matching person images captured in non-overlapping camera views. To represent person appearance, low-level visual features are sensitive to environmental changes, while high-level semantic attributes, such as ...

Exploring Deep Physiological Models for Nociceptive Pain Recognition.

Sensors (Basel, Switzerland)
Standard feature engineering involves manually designing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features for the subsequent optimisation of ...

Robust and Accurate Hand-Eye Calibration Method Based on Schur Matric Decomposition.

Sensors (Basel, Switzerland)
To improve the accuracy and robustness of hand-eye calibration, a hand-eye calibration method based on Schur matric decomposition is proposed in this paper. The accuracy of these methods strongly depends on the quality of observation data. Therefore,...

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Medical hypotheses
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MR...

Automatic grading of human blastocysts from time-lapse imaging.

Computers in biology and medicine
BACKGROUND: Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading is therefore commonly used to select the embryo with the highest implantation potential. One of the challenges,...

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model.

Neural computation
A recent paper (Chang & Tsao, 2017) reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition ...

Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.

Medical hypotheses
Liver and hepatic tumor segmentation remains a challenging problem in Computer Tomography (CT) images analysis due to its shape variation and vague boundary. The general hypothesis says that deep learning methods produce improved results on medical i...

Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI.

Medical physics
PURPOSE: Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW-MRI images is cumbers...

Classification and Diagnosis of Thyroid Carcinoma Using Reinforcement Residual Network with Visual Attention Mechanisms in Ultrasound Images.

Journal of medical systems
How to differentiate thyroid cancer nodules from a large number of benign nodules is always a challenging subject for clinicians. This paper proposes a novel Sal-deel network model to achieve the classification and diagnosis of thyroid cancer, which ...