Pathology

Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning.

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (C...

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice.

The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis softwar...

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 biopsy under the microscope. Recent research reve...

Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier.

Cervical cancer is the fourth most communal malignant disease amongst women worldwide. In maximum circumstances, cervical cancer indications are not p...

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the Uni...

Engineering approaches for characterizing soft tissue mechanical properties: A review.

From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign o...

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 melanoma (MM) in the eyelid from histopathological se...

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 degree and high degree malignancy. For high degree mal...

Image-Guided Robotic Radiosurgery for Treatment of Recurrent Grade II and III Meningiomas. A Single-Center Study.

OBJECTIVE: Stereotactic radiosurgery (SRS) has been increasingly applied for malignant meningiomas as an alternative to conventionally fractioned radi...

Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings.

Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftent...

Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.

Classification of benign-malignant lung nodules on chest CT is the most critical step in the early detection of lung cancer and prolongation of patien...

Transbronchial biopsy catheter enhanced by a multisection continuum robot with follow-the-leader motion.

PURPOSE: Current manual catheters for transbronchial biopsy in the lung lack a steering ability, which hampers a physician's ability to reach nodules ...

Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.

BackgroundManagement of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligenc...

A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network.

The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycle...

Automatically determining cause of death from verbal autopsy narratives.

BACKGROUND: A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause o...

Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease.

Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional bio...

Modeling grid fields instead of modeling grid cells : An effective model at the macroscopic level and its relationship with the underlying microscopic neural system.

A neuron's firing correlates are defined as the features of the external world to which its activity is correlated. In many parts of the brain, neuron...

Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning.

Metastasis of solid tumors is a key determinant of cancer patient survival. Targeting micrometastases using nanoparticles could offer a way to stop me...

Justifying diagnosis decisions by deep neural networks.

An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learn...

A practical guide to intelligent image-activated cell sorting.

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of singl...