Dermatology

Classification of Skin Disease using Ensemble Data Mining Techniques.

Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid development of technologies and the application of various data mining techniques in recent years, the progress of dermatological predictive classification has become more and more predictive and accurate. Therefore, development of machine learning techniques, which can effectively diff...

Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network.

The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the United States. Normally, human epidermal growth factor receptor 2 (HER2) proteins are responsible for the division and growth of healthy breast cells. HER2 status is currently assessed using immunohistochemistry (IHC) as well as in situ hybridization (I...

Data augmentation in dermatology image recognition using machine learning.

BACKGROUND: Each year in the United States, over 80 million people are affected by acne, atopic dermatitis, rosacea, psoriasis, and impetigo. Artifici...

Pathologist-level classification of histopathological melanoma images with deep neural networks.

BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reve...

Higher SNR PET image prediction using a deep learning model and MRI image.

PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model an...

The role of AI classifiers in skin cancer images.

BACKGROUND: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features ...

Evaluation of Diffusion Lesion Volume Measurements in Acute Ischemic Stroke Using Encoder-Decoder Convolutional Network.

Background and Purpose- Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed appa...

Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.

BACKGROUND AND OBJECTIVE: Skin melanoma is one of the major health problems in many countries. Dermatologists usually diagnose melanoma by visual insp...

Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.

BACKGROUND: Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role ...

Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.

OBJECTIVE: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with ...

Efficient learning from big data for cancer risk modeling: A case study with melanoma.

BACKGROUND: Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and compu...

A comparative study of deep learning architectures on melanoma detection.

Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbi...

Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning.

The stoichiometry of protein complexes is precisely regulated in cells and is fundamental to protein function. Singe-molecule fluorescence imaging bas...

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.

BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious les...

A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.

PURPOSE: This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in ...

Binary tree-like network with two-path Fusion Attention Feature for cervical cell nucleus segmentation.

Early detection of cervical lesion is of great significance in reducing mortality from cervical cancer, and segmentation of cervical cell nuclei plays...

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocar...

Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.

Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and...

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable ...

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medic...