Dermatology

Latest AI and machine learning research in dermatology for healthcare professionals.

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A multimodal deep learning model for detecting endoscopic images of near-infrared fluorescence capsules.

Early screening for gastrointestinal (GI) diseases is critical for preventing cancer development. Wi...

Editorial: New Target for Skin Cancer.

Skin cancer encompasses a diverse spectrum of malignancies with increasing global incidence and pers...

Comparative analysis of deep learning methods for breast ultrasound lesion detection and classification.

PURPOSE: Breast ultrasound (BUS) computer-aided diagnosis (CAD) systems aims to perform two major st...

Improving skin lesion classification through saliency-guided loss functions.

Deep learning has significantly advanced computer-aided diagnosis, particularly in skin lesion class...

A new diagnostic method and tool for cutaneous leishmaniasis based on artificial intelligence techniques.

BACKGROUND: Cutaneous leishmaniasis (CL) is a parasitic disease caused by protozoan parasites of the...

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targete...

Deep learning for multiple sclerosis lesion classification and stratification using MRI.

BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological disease characterized by...

Impact of artificial intelligence assisted lesion detection on radiologists' interpretation at multiparametric prostate MRI.

PURPOSE: To compare prostate cancer lesion detection using conventional and artificial intelligence ...

Self-HER2Net: A generative self-supervised framework for HER2 classification in IHC histopathology of breast cancer.

Breast cancer is a significant global health concern, where precise identification of proteins like ...

Whole Brain 3D T1 Mapping in Multiple Sclerosis Using Standard Clinical Images Compared to MP2RAGE and MR Fingerprinting.

Quantitative T1 and T2 mapping is a useful tool to assess properties of healthy and diseased tissues...

Self-supervised multi-modality learning for multi-label skin lesion classification.

BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical...

Skin Cancer Detection Using Deep Learning Approaches.

This review examined multiple deep learning (DL) methods, including artificial neural networks (ANN...

Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images.

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. H...

GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.

A major challenge in applying deep learning to medical imaging is the paucity of annotated data. Thi...

Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center.

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moon...

Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology.

BACKGROUND: Cervical cancer remains a significant global health issue, with accurate differentiation...

Machine learning based differential diagnosis of SAPHO syndrome and secondary bone tumors using whole body bone scintigraphy.

SAPHO syndrome is an inflammatory disorder with bone and cutaneous manifestations, for which whole-b...

Deep learning network enhances imaging quality of low-b-value diffusion-weighted imaging and improves lesion detection in prostate cancer.

BACKGROUND: Diffusion-weighted imaging with higher b-value improves detection rate for prostate canc...

Differentiation of canine and feline neoplasms using multi-modal imaging and machine learning.

Canine/feline (sub-)cutaneous tumors, which include lipomas, mastocytomas and soft tissue sarcomas, ...

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