AIMC Topic: Deep Learning

Clear Filters Showing 7831 to 7840 of 28423 articles

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

International journal of surgery (London, England)
OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data...

Enhanced PRIM recognition using PRI sound and deep learning techniques.

PloS one
Pulse repetition interval modulation (PRIM) is integral to radar identification in modern electronic support measure (ESM) and electronic intelligence (ELINT) systems. Various distortions, including missing pulses, spurious pulses, unintended jitters...

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based...

Automatic Skeleton Segmentation in CT Images Based on U-Net.

Journal of imaging informatics in medicine
Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct clinical contexts which can result in morphological alterations in bone structure. The visual assessment of these changes through anatomical images is co...

Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset".

Journal of imaging informatics in medicine
The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI scans. The proposed algorithm attains impressive performance on the test dataset with a mean Area Under ...

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies ...

Recognition of diabetic retinopathy and macular edema using deep learning.

Medical & biological engineering & computing
Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they can lead to permanent blindness. Traditional methods for screening these conditions rely on manual ima...

Learning shared template representation with augmented feature for multi-object pose estimation.

Neural networks : the official journal of the International Neural Network Society
Template matching pose estimation methods based on deep learning have made significant advancements via metric learning or reconstruction learning. Existing approaches primarily build distinct template representation libraries (codebooks) from render...

Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.

Diabetes & metabolism journal
BACKGRUOUND: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receive...

Fine-grained food image classification and recipe extraction using a customized deep neural network and NLP.

Computers in biology and medicine
Global eating habits cause health issues leading people to mindful eating. This has directed attention to applying deep learning to food-related data. The proposed work develops a new framework integrating neural network and natural language processi...