Pathology

Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases.

An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the "locall...

StomataCounter: a neural network for automatic stomata identification and counting.

Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user-friendly, fully automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system usin...

In-Bore Transrectal MRI-Guided Biopsy With Robotic Assistance in the Diagnosis of Prostate Cancer: An Analysis of 57 Patients.

The objective of our study was to analyze the feasibility and potential role of robotic-assisted transrectal MRI-guided biopsy for the diagnosis of p...

Hexane-1,2,5,6-tetrol as a Versatile and Biobased Building Block for the Synthesis of Sustainable (Chiral) Crystalline Mesoporous Polyboronates.

We report on the synthesis and characterization of novel mesoporous chiral polyboronates obtained by condensation of (,)/(,)-hexane-1,2,5,6-tetrol (HT...

Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression.

Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is detected at the beginning stage, it can often be c...

Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer.

IMPORTANCE: Immunohistochemistry (IHC) is the most widely used assay for identification of molecular biomarkers. However, IHC is time consuming and co...

Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow.

Introduction: The determination of tumour extent is a major challenging task in brain tumour planning and quantitative evaluation. Magnetic Resonance ...

Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks.

Differently to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same object class. In this work, ...

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System.

With the development of theories and technologies in medical imaging, most of the tumors can be detected in the early stage. However, the nature of ov...

TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning,...

Ten Years of Gabor-Domain Optical Coherence Microscopy.

Gabor-domain optical coherence microscopy (GDOCM) is a high-definition imaging technique leveraging principles of low-coherence interferometry, liquid...

Cell mitosis event analysis in phase contrast microscopy images using deep learning.

In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our metho...

Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patie...

Machine learning approaches for pathologic diagnosis.

Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general imag...

Classification of benign and malignant lung nodules from CT images based on hybrid features.

The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodule...

Recognition of early and late stages of bladder cancer using metabolites and machine learning.

INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its r...

Collateral Automation for Triage in Stroke: Evaluating Automated Scoring of Collaterals in Acute Stroke on Computed Tomography Scans.

Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more like...

A resonant tactile stiffness sensor for lump localization in robot-assisted minimally invasive surgery.

A miniature resonant tactile sensor for tissue stiffness detection in robot-assisted minimally invasive surgery is proposed in this article. The propo...

Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks.

With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly...

Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis.

PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a more accurate diagnosis in differentiating malignant ...