AIMC Topic: Deep Learning

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DeepNeuro: an open-source deep learning toolbox for neuroimaging.

Neuroinformatics
Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neu...

Study of the Usefulness of Bone Scan Index Calculated From 99m-Technetium- Hydroxymethylene Diphosphonate (Tc-HMDP) Bone Scintigraphy for Bone Metastases from Prostate Cancer Using Deep Learning Algorithms.

Current medical imaging
BACKGROUND: BSI calculated from bone scintigraphy using technetium-methylene diphosphonate (Tc-MDP) is used as a quantitative indicator of metastatic bone involvement in bone metastasis diagnosis, therapeutic effect assessment, and prognosis predicti...

A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography.

Current medical imaging
BACKGROUND: Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, whe...

Explainable Deep Learning Reproduces a 'Professional Eye' on the Diagnosis of Internal Disorders in Persimmon Fruit.

Plant & cell physiology
Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been...

Exploring generative deep learning for omics data using log-linear models.

Bioinformatics (Oxford, England)
MOTIVATION: Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for unco...

Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning.

Lab on a chip
Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D monolayer culture, the characterization of 3D tissue models from non-invasive brightfield images is a ...

A deep learning-based algorithm for detection of cortical arousal during sleep.

Sleep
STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep test...

Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations.

Medicine
Deep learning algorithms have shown excellent performances in the field of medical image recognition, and practical applications have been made in several medical domains. Little is known about the feasibility and impact of an undetectable adversaria...

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.

The Biochemical journal
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule...

Clinical concept extraction using transformers.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The goal of this study is to explore transformer-based models (eg, Bidirectional Encoder Representations from Transformers [BERT]) for clinical concept extraction and develop an open-source package with pretrained clinical models to facili...