Pathology

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image.

Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene e...

FiCRoN, a deep learning-based algorithm for the automatic determination of intracellular parasite burden from fluorescence microscopy images.

Protozoan parasites are responsible for dramatic, neglected diseases. The automatic determination of intracellular parasite burden from fluorescence microscopy images is a challenging problem. Recent advances in deep learning are transforming this process, however, high-performance algorithms have not been developed. The limitations in image acquisition, especially for intracellular parasites, mak...

Learning reduced-order models for cardiovascular simulations with graph neural networks.

Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience loss in accurac...

Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology.

Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating resear...

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.

OBJECTIVES: The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into...

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant en...

Connecting the dots towards precision orthodontics.

Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualize...

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies.

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and inter...

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safegua...

A spatial statistical framework for the parametric study of fiber networks: Application to fibronectin deposition by normal and activated fibroblasts.

Due to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative a...

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

BACKGROUND: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has sug...

Editorial for "A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI".

Liver fibrosis classification from ultrasound using machine learning: a systematic literature review.

PURPOSE: Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with advancements in medical technology and increasing ...

Deep learning-based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra.

Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within ...

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the ca...

Accurate classification of major brain cell types using in vivo imaging and neural network processing.

Comprehensive analysis of tissue cell type composition using microscopic techniques has primarily been confined to ex vivo approaches. Here, we introd...

Classification of brain tumours from MRI images using deep learning-enabled hybrid optimization algorithm.

Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the wide range of brain tumour locations, potential sha...

BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems.

PURPOSE: Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helpi...

Lightweight Multi-Class Support Vector Machine-Based Medical Diagnosis System with Privacy Preservation.

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Resea...

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guid...