Pathology

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly train machine learning models. We therefore built a data set of 185,538 breast cancer (BC) including hematoxylin and eosin (H&E) and associated immun...

Automated assessment of skin histological tissue structures by artificial intelligence in cutaneous melanoma.

BACKGROUND: Prognostic histopathological features such as mitosis in melanoma are excluded from the staging systems due to inter-observer variability and time constraints. While digital pathology offers artificial intelligence-driven solutions, existing melanoma algorithms often underperform or narrowly focus on specific features, limiting their clinical utility.

Cancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomes and Machine Learning.

Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, the curre...

scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains c...

Stability of Jurkat cells during short-term liquid storage analyzed by flow imaging microscopy.

The viability of cell-based medicinal products (CBMPs) is a critical quality attribute and must be assessed throughout the product lifecycle to contri...

Artificial intelligence-based virtual staining platform for identifying tumor-associated macrophages from hematoxylin and eosin-stained images.

BACKGROUND: Virtual staining is an artificial intelligence-based approach that transforms pathology images between stain types, such as hematoxylin an...

Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks.

Breast cancer is a leading cause of mortality worldwide. Screening therefore remains the best defense against this disease, highlighting the need for ...

Soft Biological Actuators for Meter-Scale Homeostatic Biohybrid Robots.

Skeletal muscle's elegant protein-based architecture powers motion throughout the animal kingdom, with its constituent actomyosin complexes driving in...

Harnessing the oloid shape in magnetically driven robots to enable high-resolution ultrasound imaging.

Magnetic fields enable remote manipulation of objects and are ideal for medical applications because they pass through human tissue harmlessly. This c...

Multimodal multi-instance evidence fusion neural networks for cancer survival prediction.

Accurate cancer survival prediction plays a crucial role in assisting clinicians in formulating treatment plans. Multimodal data, such as histopatholo...

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the ...

Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review.

Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for ex...

Identification of CACNB1 protein as an actionable therapeutic target for hepatocellular carcinoma via metabolic dysfunction analysis in liver diseases: An integrated bioinformatics and machine learning approach for precise therapy.

In addition to histological evaluation for nonalcoholic fatty liver disease (NAFLD), a comprehensive analysis of the metabolic landscape is urgently n...

Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance.

Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is c...

Thyroid nodule classification in ultrasound imaging using deep transfer learning.

BACKGROUND: The accurate diagnosis of thyroid nodules represents a critical and frequently encountered challenge in clinical practice, necessitating e...

Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Cancer Detection.

: Breast cancer (BC) is the most common type of cancer in women, accounting for more than 30% of new female cancers each year. Although various treatm...

A new era in nephrology: the role of super-resolution microscopy in research, medical diagnostic, and drug discovery.

For decades, electron microscopy has been the primary method to visualize ultrastructural details of the kidney, including podocyte foot processes and...

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic me...

Dataset of compression after impact testing on carbon fiber reinforced plastic laminates.

This dataset covers the data obtained from the compression after impact (CAI) tests. Before the CAI tests, the low-velocity impact (LVI) testing was c...

A lightweight PCT-Net for segmenting neural fibers in low-quality CCM images.

In this paper, we propose a lightweight Position Channel Transformer Network (PCT-Net) for segmenting slender neural fibers in low-quality corneal con...