AIMC Topic: Deep Learning

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Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasi...

The role of deep learning for periapical lesion detection on panoramic radiographs.

Dento maxillo facial radiology
OBJECTIVE: This work aimed to detect automatically periapical lesion on panoramic radiographs (PRs) using deep learning.

Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.

Tomography (Ann Arbor, Mich.)
Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implemen...

Interpreting deep learning models for glioma survival classification using visualization and textual explanations.

BMC medical informatics and decision making
BACKGROUND: Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model pr...

Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry.

Scientific reports
This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method consists of segmentation of the full liver region from computed tomography (CT) images, classificati...

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.

Scientific reports
Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespira...

Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram.

Scientific reports
The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to diff...

An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.

Scientific reports
Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area...