Pathology

Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.

Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nu...

Lipid associated antioxidants: arylesterase and paraoxonase-1 in benign prostatic hyperplasia treatment-naïve patients.

BACKGROUND: Oxidative stress and antioxidants have been implicated in many diseases including prostate cancer and benign prostatic hyperplasia (BPH). Lipid peroxidation contributes to oxidative stress. However, new and emerging antioxidants such as paraoxonase 1 (PON1) and arylesterase (ARE) associated with lipoprotein peroxidation have not been examined in BPH patients. PON1 and ARE, a high-densi...

A Hierarchical Convolutional Neural Network for vesicle fusion event classification.

Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary im...

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

Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With ...

A novel laparoscopic grasper with two parallel jaws capable of extracting the mechanical behaviour of soft tissues.

Data related to force-deformation behaviour of soft tissue plays an important role in medical/surgical applications such as realistically modelling me...

Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.

Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition method...

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.

Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability i...

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer.

Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the l...

Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome.

Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue speci...

Endomicroscopy for Computer and Robot Assisted Intervention.

Endomicroscopy is a new technique that allows human tissue to be characterized in vivo and in situ, circumventing the need for conventional biopsy and...

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.

Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognos...

MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images.

Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast can...

A rare case of isolated castrate resistant bilateral testicular metastases in advanced prostate cancer.

Testicular metastasis is rare with the prostate being the most common site of primary cancer. We report a case of a 72-year-old man with castration-re...

Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction.

Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anat...

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology.

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis ...

A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning.

Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques ha...

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied...

Two-way learning with one-way supervision for gene expression data.

BACKGROUND: A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated ...

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

BACKGROUND: Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured u...

A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment.

BACKGROUND: An initial clinical assessment is described of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intel...