Pathology

Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet.

Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimensional localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a genera...

Whole slide imaging system using deep learning-based automated focusing.

The auto focusing system, which involves moving a microscope stage along a vertical axis to find an optimal focus position, is the chief component of an automated digital microscope. Current automated focusing algorithms, especially those deployed in cost effective microscopy systems, often cannot match the efficiency of a skilled human operator in keeping a sample in focus. This work presents an ...

Automatic cancer tissue detection using multispectral photoacoustic imaging.

PURPOSE: In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive proces...

Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.

BACKGROUND: To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal c...

Microwave tomography with phaseless data on the calcaneus by means of artificial neural networks.

The aim of this study is to use a multilayer perceptron (MLP) artificial neural network (ANN) for phaseless imaging the human heel (modeled as a bilay...

Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model.

Breast cancer is a high-incidence type of cancer for women. Early diagnosis plays a crucial role in the successful treatment of the disease and the ef...

Artificial intelligence in digital breast pathology: Techniques and applications.

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopatho...

Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media.

For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is compl...

'Emulsion locks' for the containment of hydrocarbons during surfactant flushing.

Reversible double water in oil in water (W/O/W) emulsions were developed to contain subsurface hydrocarbon spills during their remediation using surfa...

Injection techniques for auricular nerve blocks in the rabbit cadaver.

OBJECTIVE: To describe the landmarks for localization and to determine the methodology and volume of methylene blue dye to adequately stain the auricu...

Editorial on "Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue".

Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning.

Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobser...

Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).

Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival ...

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal...

A modular cGAN classification framework: Application to colorectal tumor detection.

Automatic identification of tissue structures in the analysis of digital tissue biopsies remains an ongoing problem in digital pathology. Common barri...

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

OBJECTIVES: To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs)...

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient nu...

Comparison of Initial Experience with Transrectal Magnetic Resonance Imaging Cognitive Guided Micro-Ultrasound Biopsies versus Established Transperineal Robotic Ultrasound Magnetic Resonance Imaging Fusion Biopsies for Prostate Cancer.

PURPOSE: We compared cancer detection rates in patients who underwent magnetic resonance imaging cognitive guided micro-ultrasound biopsy vs robotic u...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily b...

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression.

A key feature of living tissues is their capacity to remodel and grow in response to environmental cues. Within continuum mechanics, this process can ...