AIMC Topic: Deep Learning

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Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

Cancer research and treatment
PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and...

Intraoperative surgery room management: A deep learning perspective.

The international journal of medical robotics + computer assisted surgery : MRCAS
PURPOSE: The current study aimed to systematically review the literature addressing the use of deep learning (DL) methods in intraoperative surgery applications, focusing on the data collection, the objectives of these tools and, more technically, th...

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.

NeuroImage. Clinical
The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, ...

Deep learning approach to describe and classify fungi microscopic images.

PloS one
Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional b...

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.

European radiology
OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing ker...

Accelerating quantitative MR imaging with the incorporation of B compensation using deep learning.

Magnetic resonance imaging
Quantitative magnetic resonance imaging (MRI) attracts attention due to its support to quantitative image analysis and data driven medicine. However, the application of quantitative MRI is severely limited by the long data acquisition time required b...

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.

Journal of chemical theory and computation
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of ...

A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.

Scientific reports
Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve radiologists' performance in the clinical diagnosis of breast cancer. This machine learning study develops a deep transfer learning computer-aided diagnosis (CADx) methodolo...

Computational inference of cancer-specific vulnerabilities in clinical samples.

Genome biology
BACKGROUND: Systematic in vitro loss-of-function screens provide valuable resources that can facilitate the discovery of drugs targeting cancer vulnerabilities.

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its hi...