Dermatology

Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.

Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malign...

Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data.

Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing te...

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue...

Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning.

BACKGROUND/AIMS: To develop a deep learning system (DLS) that can automatically detect malignant mel...

The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.

According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degr...

Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics.

BACKGROUND: There is a lack of research studying patient-generated data on Reddit, one of the world'...

Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.

BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical uti...

Melanoma Detection by Means of Multiple Instance Learning.

We present an application to melanoma detection of a multiple instance learning (MIL) approach, whos...

Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.

OBJECTIVE: Using CT texture analysis and machine learning methods, this study aims to distinguish th...

Using machine learning-based lesion behavior mapping to identify anatomical networks of cognitive dysfunction: Spatial neglect and attention.

Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map...

ASO Author Reflections: Robotic Pelvic Lymph Node Dissection for Metastatic Melanoma-A Minimally Invasive Approach to a Difficult Problem.

Machine learning in breast MRI.

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medic...

Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.

Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Mel...

In-Bore Transrectal MRI-Guided Biopsy With Robotic Assistance in the Diagnosis of Prostate Cancer: An Analysis of 57 Patients.

The objective of our study was to analyze the feasibility and potential role of robotic-assisted tr...

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System.

With the development of theories and technologies in medical imaging, most of the tumors can be dete...

A case of robotic assisted percutaneous coronary intervention of the left main coronary artery in a patient with very late baffle stenosis after surgical correction of anomalous left coronary artery from the pulmonary artery.

A 34-year-old woman with history of surgical correction (Takeuchi procedure) of anomalous left coron...

Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied ext...

Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.

BACKGROUND: In recent months, multiple publications have demonstrated the use of convolutional neura...

Embracing machine learning and digital health technology for precision dermatology.

Involvement from the dermatology community in the current revolution in sensors, personal health dev...

Self-Paced Balance Learning for Clinical Skin Disease Recognition.

Class imbalance is a challenging problem in many classification tasks. It induces biased classificat...

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accuratel...