AIMC Topic: Deep Learning

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Advanced analytical methods for multi-spectral transmission imaging optimization: enhancing breast tissue heterogeneity detection and tumor screening with hybrid image processing and deep learning.

Analytical methods : advancing methods and applications
Light sources exhibit significant absorption and scattering effects during the transmission through biological tissues, posing challenges in identifying heterogeneities in multi-spectral images. This paper introduces a fusion of techniques encompassi...

Psoriasis severity assessment: Optimizing diagnostic models with deep learning.

Narra J
Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis s...

Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society.

PloS one
Informal education via social media plays a crucial role in modern learning, offering self-directed and community-driven opportunities to gain knowledge, skills, and attitudes beyond traditional educational settings. These platforms provide access to...

Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.

Abdominal radiology (New York)
BACKGROUND: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used...

A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.

Abdominal radiology (New York)
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density.

Protoplasma
Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative...

Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: a proof-of-concept study.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVES: This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries and vertebral arteries in cone beam computed tomography (CBCT) scans. Additionally, a model utilizin...

Robust multi-modal fusion architecture for medical data with knowledge distillation.

Computer methods and programs in biomedicine
BACKGROUND: The fusion of multi-modal data has been shown to significantly enhance the performance of deep learning models, particularly on medical data. However, missing modalities are common in medical data due to patient specificity, which poses a...

Multimodal autism detection: Deep hybrid model with improved feature level fusion.

Computer methods and programs in biomedicine
OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intell...

Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.

Cancer science
The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes occurring in invasive areas are still not well understood. This study aimed to explore the morphologic...