Pathology

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods.

This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were used to predict the total carcass bone (TCB), total carcass fat (TCF), and total carcass muscle (TCM). The best model for predicting the three parame...

Deep Learning Neural Network-Guided Detection of Asbestos Bodies in Bronchoalveolar Lavage Samples.

INTRODUCTION: Asbestos is a global occupational health hazard, and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich proteins and mucopolysaccharides, after which they are called asbestos bodies (ABs) and can be detected in light microscopy (LM). Bronchoalveolar lava...

Analysis of cortical cell polarity by imaging flow cytometry.

Metastasis is the main cause of cancer-related death and therapies specifically targeting metastasis are highly needed. Cortical cell polarity (CCP) i...

Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM.

OBJECTIVES: Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on...

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural...

Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks.

Breast ultrasound (BUS) image classification in benign and malignant classes is often based on pre-trained convolutional neural networks (CNNs) to cop...

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry.

Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammatory bowel disease and cancer. When properly trained...

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

BACKGROUND: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Wi...

Successful Development of a Natural Language Processing Algorithm for Pancreatic Neoplasms and Associated Histologic Features.

OBJECTIVES: Natural language processing (NLP) algorithms can interpret unstructured text for commonly used terms and phrases. Pancreatic pathologies a...

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as eviden...

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study.

AIM: Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised...

Domain and Histopathology Adaptations-Based Classification for Malignancy Grading System.

Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy gla...

Novel tools for early diagnosis and precision treatment based on artificial intelligence.

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to impr...

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by micro...

Artificial intelligence in cardiac computed tomography.

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and al...

Artificial Intelligence-Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens.

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artifi...

Recent methodological advances towards single-cell proteomics.

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studie...

iOCT-guided simulated subretinal injections: a comparison between manual and robot-assisted techniques in an ex-vivo porcine model.

The purpose of this study is to compare robot-assisted and manual subretinal injections in terms of successful subretinal blistering, reflux incidence...

Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment.

Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the GLU...

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry.

In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitat...