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High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

Arthritis research & therapy
BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRS...

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Medical image analysis
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with...

Unsupervised method for normalization of hematoxylin-eosin stain in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential l...

Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy.

Analytical chemistry
Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to precisely evaluate the development stage of nonmelanoma skin cancers because they are derived from the sa...

Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection.

Cells
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are common...

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

PLoS medicine
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers....

MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.

Artificial intelligence in medicine
MOTIVATION: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of ...

Efficient deep learning model for mitosis detection using breast histopathology images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer grading. It provides vital clues to estimate the aggressiveness and the proliferation rate of the tumour. The...

Metastasis detection from whole slide images using local features and random forests.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distingu...