AIMC Topic: Image Processing, Computer-Assisted

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Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

Scientific reports
We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learni...

LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets.

Journal of biophotonics
The optoacoustic imaging (OAI) methods are rapidly evolving for resolving optical contrast in medical imaging applications. In practice, measurement strategies are commonly implemented under limited-view conditions due to oversized image objectives o...

NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images.

Computers in biology and medicine
The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable ...

Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning.

IEEE transactions on pattern analysis and machine intelligence
In this paper, the problem of describing visual contents of a video sequence with natural language is addressed. Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstru...

Gravitational Laws of Focus of Attention.

IEEE transactions on pattern analysis and machine intelligence
The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision. In this paper, we describe a model of scanpath as a dynamic process which can be interpreted as a...

Guided Attention Inference Network.

IEEE transactions on pattern analysis and machine intelligence
With only coarse labels, weakly supervised learning typically uses top-down attention maps generated by back-propagating gradients as priors for tasks such as object localization and semantic segmentation. While these attention maps are intuitive and...

Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study.

Physics in medicine and biology
Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limi...

Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks.

Physics in medicine and biology
Positron emission tomography (PET) imaging plays an indispensable role in early disease detection and postoperative patient staging diagnosis. However, PET imaging requires not only additional computed tomography (CT) imaging to provide detailed anat...

Deep-Learning-Based Segmentation and Localization of White Matter Hyperintensities on Magnetic Resonance Images.

Interdisciplinary sciences, computational life sciences
White matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual...

Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning.

JAMA network open
IMPORTANCE: Congenital adrenal hyperplasia (CAH) is the most common primary adrenal insufficiency in children, involving excess androgens secondary to disrupted steroidogenesis as early as the seventh gestational week of life. Although structural bra...