Pathology

InterDIA: Interpretable prediction of drug-induced autoimmunity through ensemble machine learning approaches.

Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these challenges, we developed InterDIA, an interpretable machine learning framework for predicting DIA toxicity based on molecular physicochemical properti...

Forensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity.

INTRODUCTION: In the reconstructive phase of medico-legal human identification, the sex estimation is crucial in the reconstruction of the biological profile and can be applied both in identifying victims of mass disasters and in the autopsy room. Due to the inherent subjectivity associated with traditional methods, artificial intelligence, specifically, convolutional neural networks (CNN) may pre...

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatmen...

Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring.

Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advan...

A machine learning based algorithm accurately stages liver disease by quantification of arteries.

A major histologic feature of cirrhosis is the loss of liver architecture with collapse of tissue and vascular changes per unit. We developed qVessel ...

Immunolipid magnetic bead-based circulating tumor cell sorting: a novel approach for pathological staging of colorectal cancer.

OBJECTIVE: This study aimed to assess whether circulating tumor cells (CTCs) from colorectal cancer (CRC) could be used as an alternative to tissue sa...

Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This ...

Monkeypox diagnosis based on probabilistic K-nearest neighbors (PKNN) algorithm.

Although it is not a new illness and has been around since the previous century, monkeypox later resurgence is fraught with difficulties. This study p...

An interpretable multi-scale convolutional attention residual neural network for glioma grading with Raman spectroscopy.

Since the malignancy of gliomas varies with their grade, classifying gliomas of different grades can assist doctors in developing personalized surgica...

Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework.

Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrat...

Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.

Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists...

The tumour histopathology "glossary" for AI developers.

The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analys...

An Artificial Intelligence-Digital Pathology Algorithm Predicts Survival After Radical Prostatectomy From the Prostate, Lung, Colorectal, and Ovarian Cancer Trial.

PURPOSE: Clinical variables alone have limited ability to determine which patients will have recurrence after radical prostatectomy (RP). We evaluated...

Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.

PURPOSE: Identification of the molecular subtypes in breast cancer allows to optimize treatment strategies, but usually requires invasive needle biops...

Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.

Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While sig...

Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with...

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnosti...

A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.

Gastric cancer (GC) is the third leading cause of cancer death worldwide. Its clinical course varies considerably due to the highly heterogeneous tumo...

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to the...

A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia.

Canine Chiari-like malformation (CM) is a neuroanatomical condition associated with conformational change of the cranium, craniocervical junction and ...