Pathology

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a C...

Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations.

While high-resolution pathology images lend themselves well to 'data hungry' deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this article, we propose a self-supervised convolutional neural network (CNN) framework to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. Our propos...

Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation.

In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and wi...

A Large-Scale Fully Annotated Low-Cost Microscopy Image Dataset for Deep Learning Framework.

This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL...

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-lea...

Improved breast cancer histological grading using deep learning.

BACKGROUND: The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decisio...

Detection of malignant melanoma in H&E-stained images using deep learning techniques.

Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large s...

Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure.

AIMS: Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can han...

New sensing platform of poly(ester-urethane)urea doped with gold nanoparticles for rapid detection of mercury ions in fish tissue.

A new electrochemical sensor has been fabricated based on the synthesis of poly(ester-urethane) urea (PUU) doped with gold nanoparticles (AuNPs), and...

Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets.

The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hypers...

Deep learning in cancer diagnosis, prognosis and treatment selection.

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patter...

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-...

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magn...

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance...

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatme...

Evaluation of Scopio Labs X100 Full Field PBS: The first high-resolution full field viewing of peripheral blood specimens combined with artificial intelligence-based morphological analysis.

BACKGROUND: Current digital cell imaging systems perform peripheral blood smear (PBS) analysis in limited regions of the PBS and require the support o...

Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images.

Oral potentially malignant disorders (OPMDs) are a group of conditions that can transform into oral cancer. The purpose of this study was to evaluate ...

Long-term effect of tocilizumab on left ventricular hypertrophy and systolic dysfunction in AA amyloidosis with rheumatoid arthritis.

Because cardiac involvement of amyloid A (AA) is not frequent, little is known about the effects of tocilizumab (TCZ; a humanized monoclonal anti-inte...

Imaging in focus: An introduction to denoising bioimages in the era of deep learning.

Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of h...

Mechanical Imaging of Soft Tissues With Miniature Climbing Robots.

Systematically mapping the mechanical properties of skin and tissue is useful for biomechanics research and disease diagnostics. For example, later st...