Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance.

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models.

Deep learning-based detection algorithm for brain metastases on black blood imaging.

Brain metastases (BM) are the most common intracranial tumors, and their prevalence is increasing. High-resolution black-blood (BB) imaging was used to complement the conventional contrast-enhanced 3D gradient-echo imaging to detect BM. In this study, we propose an efficient deep learning algorithm (DLA) for BM detection in BB imaging with contrast enhancement scans, and assess the efficacy of an automatic detection algorithm for BM.

Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model.

Objective: Mammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography.

Materials And Methods: A total of 2,208 mammography phantom images were collected for periodic accreditation of the scanner from 1,755 institutions.

Sex-Specific Differences in Lower Body Fat Distribution and Association with Physical Performance among Healthy Community-Dwelling Older Adults: A Pilot Study.

This study aims to examine sex-specific differences in body composition and lower extremity fat distribution and their association with physical performance among healthy older adults. The pilot study comprises 40 subjects (20 men and 20 women) matched by age and body mass index. The participants undergo dual-energy X-ray absorptiometry, magnetic resonance imaging, and proton magnetic resonance spectroscopy (H-MRS) to assess body composition and lower extremity fat distribution.

Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis.

Purpose: This study aimed to propose an effective end-to-end process in medical imaging using an independent task learning (ITL) algorithm and to evaluate its performance in maxillary sinusitis applications.

Materials And Methods: For the internal dataset, 2122 Waters' view X-ray images, which included 1376 normal and 746 sinusitis images, were divided into training (n=1824) and test (n=298) datasets. For external validation, 700 images, including 379 normal and 321 sinusitis images, from three different institutions were evaluated.

Longitudinal Functional Magnetic Resonance Spectroscopy Study in Subjects with Mild Cognitive Impairment and Alzheimer's Disease.

Background: Longitudinal changes of brain metabolites during a functional stimulation are unknown in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) subjects.

Objective: This study was to evaluate the longitudinal changes of brain metabolites using proton magnetic resonance spectroscopy (1H MRS) in response to treatment during a memory task in the subjects of cognitive normal (CN), aMCI, and AD.

Methods: We acquired functional magnetic resonance spectroscopy (fMRS) data from 28 CN elderly, 16 aMCI and 12 AD subjects during a face-name association task.

Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance.

Purpose: Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented.

Lifestyle-dependent brain change: a longitudinal cohort MRI study.

We investigated both independent and interconnected effects of 3 lifestyle factors on brain volume, measuring yearly changes using large-scale longitudinal magnetic resonance imaging, in middle-aged to older adults. We measured brain volumes in a cohort (n = 984, 49-79 years) from the Korean Genome and Epidemiology Study group, using baseline and follow-up estimates after 4 years. In our analysis, the accelerated brain atrophy in normal aging was observed across regions (e.

Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features.

Objective: The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer.

Materials And Methods: Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features.

Prostate cancer: correlation of intravoxel incoherent motion MR parameters with Gleason score.

Purpose: To evaluate the potential of intravoxel incoherent motion (IVIM) imaging to predict histological prognostic parameters by investigating whether IVIM parameters correlate with Gleason score.

Materials And Methods: The institutional review board approved this retrospective study, and informed consent was waived. A total of 41 patients with histologically proven prostate cancer who underwent prostate MRI using a 3T MRI machine were included.

Glutamine and Glutamate Complex, as Measured by Functional Magnetic Resonance Spectroscopy, Alters During Face-Name Association Task in Patients with Mild Cognitive Impairment and Alzheimer's Disease.

Background: The metabolite response during a memory task in Alzheimer's disease (AD) patients is unknown.

Objective: To investigate the metabolite changes in subjects with AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) elderly during a memory task using functional magnetic resonance spectroscopy (fMRS).

Methods: This study involved 23 young normal controls (YC), 24 CN elderly, 24 aMCI, and 24 mild and probable AD individuals.