Pathology

Deep learning-based analysis of gross features for ovarian epithelial tumors classification: A tool to assist pathologists for frozen section sampling.

Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge this gap, we proposed a novel approach leveraging the Swin Transformer architecture to develop a Swin-Transformer based Gross Features Detective Network (SGFD-network), which assist pathologists for locating diseased area in ovarian epithelial tumors ba...

A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease.

BACKGROUND AND OBJECTIVE: Diagnosis of rare neuromuscular diseases often relies on muscle biopsy analysis, which varies based on the evaluator's experience. Advances in deep learning show promise in improving diagnostic accuracy by identifying standardized features and phenotypic expressions in biopsy images. Explainable artificial intelligence extracts these features from the neural network's "bl...

GeNIS: A modular dataset for network intrusion detection and classification.

The development of artificial intelligence solutions for cyberattack detection and classification require high-quality and representative data. Howeve...

SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images.

Caenorhabditis elegans is a great model for exploring organismal, cellular, and subcellular biology through optical and fluorescence microscopy, with ...

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and qual...

Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.

New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is s...

Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool.

Colorectal cancer (CRC) ranks as the third most common and second deadliest cancer worldwide. The immune system, particularly tertiary lymphoid struct...

Transforming neurodegenerative disorder care with machine learning: Strategies and applications.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting in diverse forms such as memory loss and movement...

AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines.

BACKGROUND: Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approache...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not...

Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological eval...

Complex wound analysis using AI.

Impaired wound healing is a significant clinical challenge. Standard wound analysis approaches are macroscopic, with limited histological assessments ...

TriDeNT : Triple deep network training for privileged knowledge distillation in histopathology.

Computational pathology models rarely utilise data that will not be available for inference. This means most models cannot learn from highly informati...

AI-driven framework to map the brain metabolome in three dimensions.

High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the ...

Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.

OBJECTIVES: The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study ai...

A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images.

BACKGROUND: The cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-intensive, and the conclusion could be subjective and ...

Identification of prognostic subtypes and the role of FXYD6 in ovarian cancer through multi-omics clustering.

BACKGROUND: Ovarian cancer (OC), as a malignant tumor that seriously endangers the lives and health of women, is renowned for its complex tumor hetero...

Histopathology image classification based on semantic correlation clustering domain adaptation.

Deep learning has been successfully applied to histopathology image classification tasks. However, the performance of deep models is data-driven, and ...

Reduction of Acquisition Time in Fourier Transform Infrared Spectral Imaging by Deep Learning for Clinical Applications.

In infrared Fourier transform spectral imaging applied to biomedical challenges, data quality is of primary importance to achieving clinical objective...

Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study.

BACKGROUND: Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models ...