AIMC Topic: Deep Learning

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A novel approach for skin lesion symmetry classification with a deep learning model.

Computers in biology and medicine
Skin cancer has become a public health problem due to its increasing incidence. However, the malignancy risk of the lesions can be reduced if diagnosed at an early stage. To do so, it is essential to identify particular characteristics such as the sy...

Deep Learning Analysis of Histologic Images from Intestinal Specimen Reveals Adipocyte Shrinkage and Mast Cell Infiltration to Predict Postoperative Crohn Disease.

The American journal of pathology
Most patients with Crohn disease (CD), a chronic inflammatory gastrointestinal disease, experience recurrence despite treatment, including surgical resection. However, methods for predicting recurrence remain unclear. This study aimed to predict post...

Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images.

Hepatology international
BACKGROUND: Microvascular invasion (MVI) is essential for the management of hepatocellular carcinoma (HCC). However, MVI is hard to evaluate in patients without sufficient peri-tumoral tissue samples, which account for over a half of HCC patients.

The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

The British journal of radiology
OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumor...

Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography.

Medical physics
PURPOSE: Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful mo...

Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information.

Sensors (Basel, Switzerland)
Recently, the research on monocular 3D target detection based on pseudo-LiDAR data has made some progress. In contrast to LiDAR-based algorithms, the robustness of pseudo-LiDAR methods is still inferior. After conducting in-depth experiments, we real...

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy.

The Analyst
Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embed...

A Rumor Detection Method from Social Network Based on Deep Learning in Big Data Environment.

Computational intelligence and neuroscience
Aiming at the lack of feature extraction ability of rumor detection methods based on the deep learning model, this study proposes a rumor detection method based on deep learning in social network big data environment. Firstly, the scheme of combining...

Sports Economic Operation Index Prediction Model Based on Deep Learning and Ensemble Learning.

Computational intelligence and neuroscience
In order to construct a prediction model of sports economic operation indicators, this paper combines deep learning and ensemble learning algorithms to integrate and improve the algorithms and analyzes the principles of the LightGBM ensemble learning...

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.

Joint diseases and related surgery
OBJECTIVES: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology.