Pathology

A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.

Oral leukoplakia (OL) has an inherent disposition to develop oral cancer. OL with epithelial dysplasia (OED) is significantly likely to undergo malignant transformation; however, routine OED assessment is invasive and challenging. This study investigated whether a deep learning (DL) model can predict dysplasia probability among patients with leukoplakia using oral photographs. In addition, we asse...

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features.

PURPOSE: This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their effica...

Improving the diagnostic strategy for thyroid nodules: a combination of artificial intelligence-based computer-aided diagnosis system and shear wave elastography.

PURPOSE: Thyroid nodules are highly prevalent in the general population, posing a clinical challenge in accurately distinguishing between benign and m...

Artificial intelligence for detection and characterization of focal hepatic lesions: a review.

Focal liver lesions (FLL) are common incidental findings in abdominal imaging. While the majority of FLLs are benign and asymptomatic, some can be mal...

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis a...

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms.

BACKGROUND AND OBJECTIVE: Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of norm...

Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer.

Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung cancer. Out of different stages, invasive ductal BC ca...

Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting ...

De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.

BACKGROUND: Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of so...

Histopathology-driven prostate cancer identification: A VBIR approach with CLAHE and GLCM insights.

Efficient extraction and analysis of histopathological images are crucial for accurate medical diagnoses, particularly for prostate cancer. This resea...

AutoLNMNet: Automated Network for Estimating Lymph-Node Metastasis in EGC Using a Pyramid Vision Transformer and Data Derived From Multiphoton Microscopy.

Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the main...

Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors.

BACKGROUND: Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinical...

Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques.

Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatme...

PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.

Breast cancer is a significant global public health concern, with various treatment options available based on tumor characteristics. Pathological exa...

Enhancing pap smear image classification: integrating transfer learning and attention mechanisms for improved detection of cervical abnormalities.

Cervical cancer remains a major global health challenge, accounting for significant morbidity and mortality among women. Early detection through scree...

Artificial intelligence-based digital pathology for the detection and quantification of soil-transmitted helminths eggs.

BACKGROUND: Conventional microscopy of Kato-Katz (KK1.0) thick smears, the primary method for diagnosing soil-transmitted helminth (STH) infections, h...

Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C.

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal...

CNVoyant a machine learning framework for accurate and explainable copy number variant classification.

The precise classification of copy number variants (CNVs) presents a significant challenge in genomic medicine, primarily due to the complex nature of...

Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network.

BACKGROUND: The study's objective was to develop an automated method for a histopathology recognition model for odontogenic keratocysts (OKC) and non-...

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and inf...