Dermatology

Impact of fine-tuning parameters of convolutional neural network for skin cancer detection.

Melanoma skin cancer is a deadly disease with a high mortality rate. A prompt diagnosis can aid in the treatment of the disease and potentially save the patient's life. Artificial intelligence methods can help diagnose cancer at a rapid speed. The literature has employed numerous Machine Learning (ML) and Deep Learning (DL) algorithms to detect skin cancer. ML algorithms perform well for small dat...

Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.

Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 g...

Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.

BACKGROUND: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mecha...

Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.

Breast cancer is the most prevalent cancer and the second cause of cancer related death among women in the United States. Accurate and early detection...

Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning.

Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient vis...

A semi-supervised prototypical network for prostate lesion segmentation from multimodality MRI.

Prostate lesion segmentation from multiparametric magnetic resonance images is particularly challenging due to the limited availability of labeled dat...

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types.

Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk predictio...

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diabetic retinopathy and other ocular diseases. But ac...

VMKLA-UNet: vision Mamba with KAN linear attention U-Net.

In the domain of medical image segmentation, while convolutional neural networks (CNNs) and Transformer-based architectures have attained notable succ...

Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment.

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This s...

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains lim...

Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation.

Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during t...

Recent advances in cutaneous drug delivery by iontophoresis.

INTRODUCTION: Iontophoresis has been extensively studied for topical and transdermal drug delivery to stimulate the absorption of molecules that would...

Machine Learning Models of Early Longitudinal Toxicity Trajectories Predict Cetuximab Concentration and Metastatic Colorectal Cancer Survival in the Canadian Cancer Trials Group/AGITG CO.17/20 Trials.

PURPOSE: Cetuximab (CET), targeting the epidermal growth factor receptor, is a systemic treatment option for patients with colorectal cancer. One know...

Pre-trained molecular representations enable antimicrobial discovery.

The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of ...

Exploring 4 generation EGFR inhibitors: A review of clinical outcomes and structural binding insights.

Epidermal growth factor receptor (EGFR) is a potential target for anticancer therapies and plays a crucial role in cell growth, survival, and metastas...

Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside o...

Application and research progress of artificial intelligence in allergic diseases.

Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textu...

Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management.

Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality of life of patients. The latest advances in artific...

Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification.

OBJECTIVES: Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors worldwide. However, early diagnosis is complex owin...