AIMC Topic: Image Processing, Computer-Assisted

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Automated Recognition of Cancer Tissues through Deep Learning Framework from the Photoacoustic Specimen.

Contrast media & molecular imaging
The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional ...

Learning low-dose CT degradation from unpaired data with flow-based model.

Medical physics
BACKGROUND: There has been growing interest in low-dose computed tomography (LDCT) for reducing the X-ray radiation to patients. However, LDCT always suffers from complex noise in reconstructed images. Although deep learning-based methods have shown ...

A real-time object detection model for orchard pests based on improved YOLOv4 algorithm.

Scientific reports
Accurate and efficient real-time detection of orchard pests was essential and could improve the economic benefits of the fruit industry. The orchard pest dataset, PestImgData, was built through a series of methods such as web crawler, specimen image ...

Dermoscopy and skin imaging light sources: a comparison and review of spectral power distribution and color consistency.

Journal of biomedical optics
SIGNIFICANCE: Dermoscopes incorporate light, polarizers, and optical magnification into a handheld tool that is commonly used by dermatologists to evaluate skin findings. Diagnostic accuracy is improved when dermoscopes are used, and some major artif...

Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning Approaches.

IEEE/ACM transactions on computational biology and bioinformatics
Histopathological identification of tumor tissue is one of the routine pathological diagnoses for pathologists. Recently, computational pathology has been successfully interpreted by a variety of deep learning-based applications. Nevertheless, the hi...

Fuzzy Edge-Detection as a Preprocessing Layer in Deep Neural Networks for Guitar Classification.

Sensors (Basel, Switzerland)
Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination ...

DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning.

Medicine and science in sports and exercise
PURPOSE: Muscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the...

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.

Scientific reports
Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM's high affinity with various analytical options into account. As is well...

New deep learning method for efficient extraction of small water from remote sensing images.

PloS one
Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other inte...

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

International journal of radiation oncology, biology, physics
PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images.