Pathology

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo.

Toxicopathological images acquired during safety assessment elucidate an individual's biological res...

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.

BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. Howev...

AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation.

Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical ...

Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023).

BACKGROUND AND OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with ...

Advances in melanoma: epidemiology, diagnosis, and prognosis.

Unraveling the multidimensional complexities of melanoma has required concerted efforts by dedicated...

A Semi-Autonomous Stereotactic Brain Biopsy Robotic System With Enhanced Surgical Safety and Surgeon-Robot Collaboration.

OBJECTIVE: Despite benefits brought by recent neurosurgical robots, surgical safety and surgeon-robo...

[Multistain deep learning as a prognostic and predictive biomarker in colorectal cancer].

The tumor immune microenvironment (TIME) plays a crucial prognostic and predictive role in solid mal...

Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis.

Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research w...

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagno...

ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images.

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell st...

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...

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 e...

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 capt...

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), radiomic...

Deep learning based histological classification of adnex tumors.

BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the ha...

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. ...

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 co...

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...

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 ...

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 quant...