Pathology

Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.

OBJECTIVE: Radiogenomics aims to non-invasively predict tumour genotypes from imaging, but most studies assume molecular homogeneity by assigning a single biopsy-derived label to all lesions within a patient. This approach risks substantial label noise given well-documented interlesional heterogeneity. We investigated whether anchoring training to biopsy-confirmed lesions improves radiogenomic mod...

Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.

Single-molecule localization microscopy (SMLM) enables volumetric nanoscopy by retrieving 3D molecular positions from engineered 2D fluorescence patterns. However, achieving nanoscale resolution over large axial ranges in complex samples remains challenging due to optical aberrations and overlapping signals from high-density molecules. Here, we introduce LUNAR, a self-supervised neural-physics fra...

A hyperspectral imaging framework integrating band selection and deep learning for beverage stain classification in forensic analysis.

The technique of Hyperspectral Imaging (HSI) is significant in the field of non-destructive forensic crime scene investigation, as it allows for the i...

Machine learning-based integrative analysis identifies CXCL13-driven tertiary lymphoid structures as favorable immune and prognostic features in osteosarcoma.

BACKGROUND: Osteosarcoma, an aggressive bone malignancy with limited response to immunotherapy, remains a major clinical challenge. Tertiary lymphoid ...

A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.

BACKGROUND: Malaria remains a significant global health challenge, requiring diagnostic approaches that are rapid, cost-effective, and accurate. The p...

Progress in Precision Drug Delivery Systems for Neurodegenerative Diseases with Muscle Atrophy.

Neurodegenerative diseases frequently co-occur with skeletal muscle atrophy, creating a complex comorbid condition that significantly accelerates func...

AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney.

Identification of tissue-region-specific changes in glycosylation is crucial for understanding the pathogenesis of kidney diseases, yet it remains a g...

Machine learning for predicting young's modulus of soft tissues from atomic force microscopy data.

Atomic force microscopy (AFM) is an advanced tool applied to investigate the mechanical properties of a wide variety of material classes, including so...

Single-cell and spatial analyses identify a glycolysis-high state driven by STOML2 in epithelial ovarian cancer.

OBJECTIVE: Metabolic heterogeneity contributes to therapeutic resistance and poor prognosis in epithelial ovarian cancer (EOC), yet the regulatory dri...

Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.

While cell shape fundamentally governs tissue function, the underlying links between single-cell shape and protein expression have been difficult to r...

Thiazolyl Blue as a High-Efficiency Leveler for Conformal Copper Electrodeposition in High-Aspect-Ratio PCB Through-Holes.

Driven by the rapid development of 5G/6G communications, high-performance computing, and artificial intelligence, the metallization of high-aspect-rat...

Advancing equity in endometrial cancer: A narrative synthesis using a cluster-informed framework for resource-stratified implementation.

Endometrial cancer incidence and mortality are rising globally, disproportionately affecting health systems facing diagnostic, therapeutic, and surviv...

T2-FLAIR digital subtraction radiomics versus neuroradiologist visual assessment for differentiating IDH-mutant astrocytomas from other non-enhancing low-grade gliomas: An externally validated machine learning study.

PURPOSE: Non-invasive differentiation of isocitrate dehydrogenase (IDH)-mutant, 1p/19q non-codeleted astrocytomas from other non-enhancing low-grade g...

A Novel Fluoride Tray-Assisted Workflow for AI-driven Automated Maxillary Gingival Segmentation on CBCT.

OBJECTIVES: To clinically validate an AI-based tool for automated maxillary gingival segmentation of marginal and supracrestal gingiva on cone beam co...

Integrating spatial and single-cell transcriptomics via machine learning to characterize efferocytosis in hepatocellular carcinoma prognosis and immunotherapy.

BACKGROUND: The intratumoral heterogeneity and immunosuppressive microenvironment of hepatocellular carcinoma (HCC) significantly limit therapeutic ef...

Artificial intelligence with deep learning driven entropy-curvature attention mechanism for detection and segmentation of skin lesions using biomedical images.

In medical image analysis, skin lesion diagnosis remains a complicated task. Skin lesions are a prevalent form of skin disease that exists globally. A...

Multicentric data challenge for artificial intelligence-based classification of leukocytes: results from the CytologIA consortium.

Accurate morphological classification of blood leukocytes remains a cornerstone of hematological diagnostics but still relies on manual expertise, lea...

NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.

Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains cha...

Identification and diagnosis of flow instability in parallel channel systems using machine learning.

Density wave oscillations in parallel thermal-fluid systems threaten operational safety due to uneven flow and instability. Predicting these oscillati...

HESpotEx: a dual-stream deep learning framework for spot-level gene expression prediction from histological images.

Whole-slide histopathological images (WSIs) constitute a fundamental approach in disease diagnosis and prognosis. Recently emerging spatial transcript...