AIMC Topic: Deep Learning

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Deep Learning-Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity.

Translational vision science & technology
PURPOSE: Two-photon excitation fluorescence (2PEF) reveals information about tissue function. Concerns for phototoxicity demand lower light exposure during imaging. Reducing excitation light reduces the quality of the image by limiting fluorescence e...

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

JCO clinical cancer informatics
PURPOSE: Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatme...

Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The Journal of the Acoustical Society of America
The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize ...

Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall.

Journal of biomedical optics
SIGNIFICANCE: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumpt...

Can 3D artificial intelligence models outshine 2D ones in the detection of intracranial metastatic tumors on magnetic resonance images?

Journal of the Chinese Medical Association : JCMA
BACKGROUND: This study aimed to compare the prediction performance of two-dimensional (2D) and three-dimensional (3D) semantic segmentation models for intracranial metastatic tumors with a volume ≥ 0.3 mL.

Avoiding a replication crisis in deep-learning-based bioimage analysis.

Nature methods
Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular...

Identification of glaucoma from fundus images using deep learning techniques.

Indian journal of ophthalmology
PURPOSE: Glaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual ...

Transfer Deep Learning for Dental and Maxillofacial Imaging Modality Classification: A Preliminary Study.

The Journal of clinical pediatric dentistry
OBJECTIVE: To apply the technique of transfer deep learning on a small data set for automatic classification of X-ray modalities in dentistry.

Achieving Expert-Level Interpretation of Serum Protein Electrophoresis through Deep Learning Driven by Human Reasoning.

Clinical chemistry
BACKGROUND: Serum protein electrophoresis (SPE) is a common clinical laboratory test, mainly indicated for the diagnosis and follow-up of monoclonal gammopathies. A time-consuming and potentially subjective human expertise is required for SPE analysi...

Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review.

JAMA otolaryngology-- head & neck surgery
IMPORTANCE: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clin...