AIMC Topic: Deep Learning

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Neural negated entity recognition in Spanish electronic health records.

Journal of biomedical informatics
This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial in...

A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.

Journal of applied clinical medical physics
PURPOSE: To develop and test a three-dimensional (3D) deep learning model for predicting 3D voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).

Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.

Translational vision science & technology
PURPOSE: To evaluate the role of ensemble learning techniques with deep learning in classifying diabetic retinopathy (DR) in optical coherence tomography angiography (OCTA) images and their corresponding co-registered structural images.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

PLoS computational biology
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image an...

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network.

PloS one
Zanthoxyli Pericarpium (ZP) are the dried ripe peel of Zanthoxylum schinifolium Sieb. et Zucc (ZC) or Zanthoxylum bungeanum Maxim (ZB). It has wide range of uses both medicine and food, and favorable market value. The diverse specifications of compon...

Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images.

European journal of radiology
PURPOSE: We aimed to propose a highly automatic and objective model named deep learning Radiomics of thyroid (DLRT) for the differential diagnosis of benign and malignant thyroid nodules from ultrasound (US) images.

Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique.

International journal of cardiology
BACKGROUND: Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automa...

Noise can speed backpropagation learning and deep bidirectional pretraining.

Neural networks : the official journal of the International Neural Network Society
We show that the backpropagation algorithm is a special case of the generalized Expectation-Maximization (EM) algorithm for iterative maximum likelihood estimation. We then apply the recent result that carefully chosen noise can speed the average con...

A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: The purpose of this study is to evaluate the performance of a novel deep learning (DL) tool for fully automated measurements of the sagittal spinopelvic balance from X-ray images of the spine in comparison with manual measurements.

Predicting Inpatient Medication Orders From Electronic Health Record Data.

Clinical pharmacology and therapeutics
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train ...