AIMC Topic: Male

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Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.

Scientific reports
Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and seco...

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis.

Contrast media & molecular imaging
This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer...

Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.

Frontiers in endocrinology
BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise ...

Predicting self-intercepted medication ordering errors using machine learning.

PloS one
Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to mis...

Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.

The Journal of investigative dermatology
Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied ...

Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM.

Sensors (Basel, Switzerland)
A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with con...

Outcomes of robot-assisted urinary sphincter implantation for male neurogenic urinary incontinence.

BJU international
OBJECTIVES: To report the functional outcomes of robot-assisted laparoscopic artificial urinary sphincter implantation (R-AUS) in men with neurogenic stress urinary incontinence (SUI).

Initial Experience with Robotic Inguinal Hernia Repair in the Adolescent Population.

Journal of laparoendoscopic & advanced surgical techniques. Part A
There is no one standard procedure encompassing the needs and differences of the entire pediatric population for inguinal hernia repair (IHR). Several techniques can be used, including open repair, laparoscopic, and robotic-assisted laparoscopic rep...

[MRI-guided minimally invasive treatment of prostate cancer].

Der Radiologe
CLINICAL/METHODOLOGICAL ISSUE: Multiparametric magnetic resonance imaging (mpMRI) of the prostate plays a crucial role in the diagnosis and local staging of primary prostate cancer.

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks.

Journal of digital imaging
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their co...