AIMC Topic: Deep Learning

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Essentially unedited deep-learning-based OARs are suitable for rigorous oropharyngeal and laryngeal cancer treatment planning.

Journal of applied clinical medical physics
Quality of organ at risk (OAR) autosegmentation is often judged by concordance metrics against the human-generated gold standard. However, the ultimate goal is the ability to use unedited autosegmented OARs in treatment planning, while maintaining th...

AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators.

Neural networks : the official journal of the International Neural Network Society
Adjoint operators have been found to be effective in the exploration of CNN's inner workings (Wan and Choe, 2022). However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an ...

Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels.

Photodiagnosis and photodynamic therapy
BACKGROUND: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep learning (DL)-based noise reduction algorithm, on enhanced depth imaging-optical coherence tomography (EDI-OCT) images with different noise levels.

Automated segmentation of phases, steps, and tasks in laparoscopic cholecystectomy using deep learning.

Surgical endoscopy
BACKGROUND: Video-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requi...

Deep learning(s) in gaming disorder through the user-avatar bond: A longitudinal study using machine learning.

Journal of behavioral addictions
BACKGROUND AND AIMS: Gaming disorder [GD] risk has been associated with the way gamers bond with their visual representation (i.e., avatar) in the game-world. More specifically, a gamer's relationship with their avatar has been shown to provide relia...

Classification of tastants: A deep learning based approach.

Molecular informatics
Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and...

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition.

Sensors (Basel, Switzerland)
Sign language recognition, an essential interface between the hearing and deaf-mute communities, faces challenges with high false positive rates and computational costs, even with the use of advanced deep learning techniques. Our proposed solution is...

Efficient automated error detection in medical data using deep-learning and label-clustering.

Scientific reports
Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, li...

Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.

Scientific reports
Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present s...

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Scientific reports
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features...