Pathology

Predicting placenta accreta spectrum antenatally: a deep learning-radiomics dual-model based on intraoperative clinical and histopathologic criteria.

PURPOSE: Definitive diagnosis of placenta accreta spectrum (PAS) disorders occurs at delivery by using combined intraoperative clinical and histopathologic diagnostic criteria per the recently updated International Federation of Gynecology and Obstetrics (FIGO) classification system. The purpose of this study is to construct a dual diagnostic model based on intraoperative clinical and histopatholo...

Artificial intelligence-based quantification of epidermal proliferation and apoptosis in human skin.

Although artificial intelligence is rapidly advancing, its application in translational dermatological research remains limited. In this study, we established an artificial intelligence-based image analysis workflow for the quantification of proliferation and apoptosis within the human epidermis using 3,3'-diaminobenzidine-based immunohistochemical staining. Human skin cultured in a 3-dimensional ...

Glycosylation-related gene risk model and functional validation of OSTC and TUBA1C in lung adenocarcinoma.

INTRODUCTION: OSTC and TUBA1C drive the malignant progression of lung adenocarcinoma (LUAD) through the modulation of N-glycosylation and the PI3K/AKT...

Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB2.

Transition metal diboride (TMB2) ceramics combine high hardness with excellent thermal and chemical stability, making them attractive for protective c...

Follicle Identification in Primate Ovaries Via Machine Learning†.

Studies of ovarian health and aging rely on estimates of the ovarian reserve, i.e. the number of healthy ovarian follicles. We present a machine learn...

Clinical neuroanatomy teaching at Peking University Health Science Center: Evolution, current practice, and future direction.

Neuroanatomy has been considered one of the most challenging subjects in medical education, often leading to "neurophobia" and discouraging students f...

Noble Metal-Metal Oxide Nanohybrids as a High-Performance LDI-MS Matrix for Machine Learning-Driven Metabolic Diagnosis of Esophageal Cancer.

Esophageal cancer represents a global health challenge with a notably high incidence and poor prognosis, necessitating the development of rapid, nonin...

A Generative Multi-modal Deep Learning Framework for Non-invasive Prediction of Ki-67 Expression in Papillary Thyroid Microcarcinoma Using Preoperative Ultrasound.

Accurate assessment of Ki-67 expression plays a critical role in the risk stratification and prognosis of papillary thyroid microcarcinoma (PTMC). How...

Precision Oncology in Northern Germany - Integrating Multi-Omics and AI to Support Evidence-Based Molecular Tumor Boards.

The expanding availability of multi-omics profiling and advances in artificial intelli-gence (AI) and machine learning (ML) are changing precision onc...

The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population.

Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do ex...

A Noise-Aware Robustness Evaluation Framework for Breast Cancer Classification in Ultrasound Imaging.

Accurate detection of breast cancer is essential, as it remains a leading cause of cancer-related mortality worldwide. Ultrasound is adopted due to it...

From stars to molecules: AI guided device-agnostic super-resolution imaging.

Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies. However, existing super-resolu...

A multimodal optical microscopy dataset for characterizing cytoskeletal organization.

Optical microscopy combined with machine learning for image analysis is seeing increasing use for studying cell morphology and the organization of sub...

BASG-Net: a background-aware spatial gating network for cervical cell segmentation in pap smear images.

The precise segmentation of cervical cell images is regarded as a fundamental prerequisite for the implementation of computer-aided cervical cancer sc...

Machine learning-based electrofacies characterization of the Asmari reservoir in the Ahvaz oil field (SW Iran) using multi-resolution graph-based clustering (MRGC) and hydraulic flow unit (HFU) analysis.

Electrofacies (EF) analysis has become an effective approach for reservoir characterization, particularly in heterogeneous reservoirs where core data ...

RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.

Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-depen...

Reversing cancer cell behavior using AI-guided CRISPR and quantum nanobiology: a systems-based approach to epigenetic reprogramming.

Treatment effectiveness is hindered by the phenotypic plasticity of cancer and the genetic complexity of tumors. However, CRISPR-Cas-based medicines f...

BiCervi: Pap smear microscopy image dataset for cervical cancer detection and classification.

Cervical cancer is still a major public-health challenge, especially in low and middle-income countries where there are limited numbers of specialists...

Multistage microrobots with pH-responsive release of platelet membrane-coated nanoparticles.

Targeted drug delivery in the gastrointestinal tract remains challenging because therapeutics must overcome multiple hierarchical barriers before reac...

From unlabeled to labeled: self-supervised deep learning in computational pathology.

PURPOSE: The advancement of decision support systems for pathology and their implementation in clinical practice have been limited by the necessity fo...