Pathology

Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

The objective of this study was to assess the impact of artificial intelligence (AI)-based decision support (DS) on breast ultrasound (US) lesion assessment. A multicenter retrospective review of 900 breast lesions (470/900 [52.2%] benign; 430/900 [47.8%] malignant) on US by 15 physicians (11 radiologists, two surgeons, two obstetrician/gynecologists). An AI system (Koios DS for Breast, Koios Me...

Deep learning for mass detection in Full Field Digital Mammograms.

In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) mod...

Classifying changes in LN-18 glial cell morphology: a supervised machine learning approach to analyzing cell microscopy data via FIJI and WEKA.

In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information...

Natural Language Generation Model for Mammography Reports Simulation.

Extending the size of labeled corpora of medical reports is a major step towards a successful training of machine learning algorithms. Simulating new ...

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes.

Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor...

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures ...

Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.

Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more li...

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines.

Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively qu...

[Deep Learning in Pathology: Applications and Challenges in Ophthalmic Pathology].

INTRODUCTION: Deep learning has received increasing attention in recent years and is used in many different areas. Since image analysis is a strength ...

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods.

Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation...

Cellular community detection for tissue phenotyping in colorectal cancer histology images.

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development o...

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, wh...

Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images.

PURPOSE: We aimed to propose a highly automatic and objective model named deep learning Radiomics of thyroid (DLRT) for the differential diagnosis of ...

Deep Learning for Ultrasound Localization Microscopy.

By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the s...

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

PURPOSE: Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3...

Machine-learning-based quality control of contractility of cultured human-induced pluripotent stem-cell-derived cardiomyocytes.

The precise and early assessment of cardiotoxicity is fundamental to bring forward novel drug candidates to the pharmaceutical market and to avoid the...

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in child...

A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms.

Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immuno...

Artificial intelligence and machine learning in nephropathology.

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract in...

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides.

IMPORTANCE: Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An ...