Dermatology

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated r...

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers.

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential role. As current gene ontology is based predominantly on protein-level informati...

A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs.

Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The inte...

Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images.

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an ...

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma.

BACKGROUND: Immunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robus...

Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach.

Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions...

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic ap...

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina.

Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, necessitates the precise segmentation of lesions for the effective gradi...

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.

BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient sel...

Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?

Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and...

Skinformatics: Navigating the big data landscape of dermatology.

Big data and associated approaches to analyse it are on the rise, especially in healthcare settings. This growth is also seen with unique applications...

Analysis of international publication trends in artificial intelligence in skin cancer.

Bibliometric methods were used to analyze publications on the use of artificial intelligence (AI) in skin cancer from 2010 to 2022, aiming to explore ...

Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.

PURPOSE: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening ...

The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies.

OBJECTIVE: The incidence of non-melanoma skin cancers, encompassing basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), is on the...

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.

Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rate...

Diagnostic accuracy of dental caries detection using ensemble techniques in deep learning with intraoral camera images.

Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma ...

Feature-Based vs. Deep-Learning Fusion Methods for the In Vivo Detection of Radiation Dermatitis Using Optical Coherence Tomography, a Feasibility Study.

Acute radiation dermatitis (ARD) is a common and distressing issue for cancer patients undergoing radiation therapy, leading to significant morbidity....

Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.

Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by d...

Deep Location Soft-Embedding-Based Network With Regional Scoring for Mammogram Classification.

Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. C...