Pathology

Classification of melanoma skin Cancer based on Image Data Set using different neural networks.

This paper aims to address the pressing issue of melanoma classification by leveraging advanced neural network models, specifically basic Convolutional Neural Networks (CNN), ResNet-18, and EfficientNet-B0. Our objectives encompass presenting and evaluating these models based on established practices in medical image diagnosis. Additionally, we aim to demonstrate their effectiveness in contributin...

Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.

BACKGROUND: Breast cancer (BC) is caused by the uncontrolled proliferation of breast epithelial cells followed by malignant transformation, and it has the highest incidence among female malignant tumors. The metastasis of BC occurs through direct and lymphatic spread. Although ocular metastasis is relatively rare, it is a good indicator of a worse prognosis. We used machine learning (ML) to establ...

Multiple-Instance Learning for thyroid gland disease classification: A hands-on experience.

The morphological diagnosis of thyroid gland neoplasms presents a dual challenge: it requires the expertise of highly trained specialists and consider...

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification.

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior perfo...

Role of Artificial Intelligence for Colon Polyp Detection and Diagnosis and Colon Cancer.

The broad use of artificial intelligence (AI) and its various applications have already shown significant impact in medicine and in everyday life. In ...

A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

BACKGROUND:  Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmona...

Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.

Machine learning (ML) is currently being widely studied and applied in data analysis and prediction in various fields, including laboratory medicine. ...

Virtual histopathology methods in medical imaging - a systematic review.

Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more pre...

Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and Machine-Learning Analysis.

Two-photon (2P) fluorescence lifetime imaging microscopy (FLIM) was used to track cellular metabolism with drug treatment of auto-fluorescent coenzyme...

Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation.

Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve p...

An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning.

Ultrasonography (US) of thyroid nodules is often time consuming and may be inconsistent between observers, with a low positivity rate for malignancy i...

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence.

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and expl...

A deep neural network improves endoscopic detection of laterally spreading tumors.

BACKGROUND: Colorectal cancer (CRC) is the malignant tumor of the digestive system with the highest incidence and mortality rate worldwide. Laterally ...

The potential of AI-assisted gastrectomy with dual highlighting of pancreas and connective tissue.

BACKGROUND: Standard gastrectomy with D2 lymph node (LN) dissection for gastric cancer involves peripancreatic lymphadenectomy [1]. This technically d...

Image-based Artificial Intelligence models in the diagnosis and classification of vascular anomalies of the soft tissue in the head and neck.

BACKGROUND: The International Society for the Study of Vascular Anomalies (ISSVA) provides a detailed classification system to facilitate accurate dia...

AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks.

UNLABELLED: Vulvovaginal candidiasis (VVC) is a prevalent fungal ailment affecting women globally. Timely and accurate diagnosis is crucial. Tradition...

Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms.

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the leading causes of morbidity and mortality. Revasculari...

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images.

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts' cells to replicate themselves. Despite having simplistic geneti...

Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (...

Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images.

Neuroendocrine neoplasms (NENs) arise from diffuse neuroendocrine cells and are categorized as either well-differentiated and less proliferative Neuro...