AIMC Topic: Deep Learning

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Comparative analysis of deep learning methods for lesion detection on full screening mammography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Breast cancer is the most prevalent type of cancer in women. Although mammography is used as the main imaging modality for the diagnosis, robust lesion detection in mammography images is a challenging task, due to the poor contrast of the lesion boun...

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatric complexities. Mobility limitations are amongst the most frequent and early markers of MS. We eval...

Feature extraction of time series data on functional near-infrared spectroscopy and comparison of deep learning performance for classifying patients with Alzheimer's-related mild cognitive impairment: a post-hoc analysis of a diagnostic interventional trial.

European review for medical and pharmacological sciences
OBJECTIVE: This study aimed to define a method of classifying patients with mild cognitive impairment caused by Alzheimer's disease by the retrieval of functional near-infrared spectroscopy (fNIRS) signal characteristics obtained during olfactory sti...

Using deep learning to track time × frequency whistle contours of toothed whales without human-annotated training data.

The Journal of the Acoustical Society of America
Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including speci...

Mechanism of liver X receptor α and ATP binding cassette transporter A1 involved in preeclampsia using an optimized deep learning model.

European review for medical and pharmacological sciences
OBJECTIVE: Preeclampsia (PE) is a complex disease-causing multisystem damage. Many genes, environmental factors, and their interactions are involved in the development and progression of PE. The pathogenesis of PE is not fully understood, limiting th...

Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.

Clinical and translational medicine
INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. ...

Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance.

Korean journal of radiology
In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object de...

Untrained deep learning-based differential phase-contrast microscopy.

Optics letters
Quantitative differential phase-contrast (DPC) microscopy produces phase images of transparent objects based on a number of intensity images. To reconstruct the phase, in DPC microscopy, a linearized model for weakly scattering objects is considered;...