AIMC Topic: Deep Learning

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Biology-guided deep learning predicts prognosis and cancer immunotherapy response.

Nature communications
Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatmen...

A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population.

Scientific reports
Digital images allow for the objective evaluation of facial appearance and abnormalities as well as treatment outcomes and stability. With the advancement of technology, manual clinical measurements can be replaced with fully automatic photographic a...

A bi-layer model for identification of piwiRNA using deep neural learning.

Journal of biomolecular structure & dynamics
piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has th...

Deep learning models for webcam eye tracking in online experiments.

Behavior research methods
Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning...

THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution.

The Journal of arthroplasty
BACKGROUND: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either ...

Accelerated 3D MR neurography of the brachial plexus using deep learning-constrained compressed sensing.

European radiology
OBJECTIVES: To explore the use of deep learning-constrained compressed sensing (DLCS) in improving image quality and acquisition time for 3D MRI of the brachial plexus.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

European radiology
OBJECTIVES: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. ...

An overview of ultrasound-derived radiomics and deep learning in liver.

Medical ultrasonography
Over the past few years, developments in artificial intelligence (AI), especially in radiomics and deep learning, have enabled the extraction of pathophysiology-related information from varied medical imaging and are progressively transforming medica...

Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared w...

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

European journal of nuclear medicine and molecular imaging
OBJECTIVE: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).