Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images.

infection (CDI) is an enteric bacterial disease that is increasing in incidence worldwide. Symptoms of CDI range from mild diarrhea to severe life-threatening inflammation of the colon. While antibiotics are standard-of-care treatments for CDI, they are also the biggest risk factor for development of CDI and recurrence.

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.

In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions.

A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images.

Tissue segmentation in whole-slide images is an important task in digital pathology, required for efficient and accurate computer-aided diagnostics. Precise tissue segmentation is particularly significant for a correct diagnosis in cases, when tissue structure of a specimen is very porous, such as skin specimens. In this paper, we addressed the problem of fore- and background segmentation in histopatological images of skin specimens stained with hematoxylin and eosin (H&E), which has not been solved yet, by a novel method based on a combination of statistical analysis, color thresholding, and binary morphology.

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

Background: Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet.