Pathology

Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification.

One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the imaging modalities is always subjective, and this paves the way for deep learning assisted computer aided techniques. Still, the accuracy of such a technique is the major concern. This research work attempts to enhance lung cancer diagnostics from hi...

GR2ST: Spatial Transcriptomics Prediction based on Graph-Enhanced Multimodal Contrastive Learning.

MOTIVATION: Spatial transcriptomics techniques capture gene expression data and spatial coordinates, while simultaneously correlating them with tissue section images. This advantage makes Spatial transcriptomics data highly valuable for research, such as investigating disease mechanisms and cancer prognosis. However, the extended time and high cost of spatial transcriptomic sequencing currently li...

Data-driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology.

Alzheimer's disease neuropathological changes (ADNC)-operationalized with semi-quantitative parameters-represent the consensus-based gold standard for...

ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.

BACKGROUND: Optimization of biotechnological processes is traditionally limited by time-consuming trial-and-error approaches and the complexity of sim...

Predicting DNA replication-related protein expression at the nucleus level from HE: A deep learning study with same-specimen paired data.

BACKGROUND: The inference of molecular information from hematoxylin-eosin (HE) specimens may reduce the ancillary testing burden in digital pathology....

Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.

Here the current and emerging roles of brain positron emission tomography (PET) in Alzheimer's disease (AD) in the era of anti-amyloid-β antibody ther...

Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?

Pancreatic cystic lesions (PCLs) are increasingly detected due to the widespread use of cross-sectional imaging and represent a significant diagnostic...

Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.

OBJECTIVES: The aim of this study was to develop and validate an MRI radiomics-based predictive model to discriminate significant prostate cancer (sPC...

Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps.

BACKGROUND: To develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpret...

MMP9 as a shared immune-related gene in Alzheimer's and Huntington's diseases: a cross-tissue transcriptomic analysis.

Alzheimer's disease (AD) and Huntington's disease (HD) share neuroinflammatory mechanisms, yet their specific immune microenvironments remain poorly u...

Artificial intelligence-based reclassification of gastric adenocarcinoma enables prognostic stratification via diffuse-type patch proportion.

BACKGROUND: Gastric adenocarcinoma (GAC) remains a major global health burden with marked heterogeneity, complicating diagnosis and prognostic assessm...

Artificial Intelligence for histopathological diagnosis and grading of breast cancer in Ethiopia.

BACKGROUND: Recent advances in computational pathology enables AI-assisted diagnosis and risk stratification of breast cancer. This advance in technol...

Machine learning-based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening.

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study l...

DUSTrack: Semi-automated point tracking in ultrasound videos.

UNLABELLED: Ultrasound technology enables safe, non-invasive imaging of dynamic tissue behavior, making it a valuable tool in medicine, biomechanics, ...

Predicting Agitation Stability of Monoclonal Antibodies during Developability Assessment.

Developability assessment facilitates the selection of antibody drug candidates with desirable pharmaceutical properties. However, it remains uncertai...

Neural cognitive diagnosis modeling incorporating response times.

Cognitive diagnosis is a fundamental issue in the field of intelligent education, aiming to identify students' mastery of specific knowledge concepts....

Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment ...

Deep learning for predicting pituitary neuroendocrine tumour lineage and high-risk subtypes from histology.

Pituitary neuroendocrine tumours (PitNETs) exhibit significant heterogeneity, posing challenges for clinical management. We developed a deep learning ...

Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.

Currently, bone cancer remains a big challenge in healthcare, early and accurate diagnosis is therefore key to achieving the required treatment outcom...

3D vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning.

Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D...