Pathology

ReVGG-R2Net: Optimized recurrent framework for microscopic blood cell segmentation.

Accurate segmentation of microscopic blood cell images is critical for advancing biomedical analysis and diagnostics. However, existing segmentation models often fall short due to limited dataset diversity and challenges in capturing fine cellular details, especially when different cell types are closely positioned. This paper addresses these challenges by introducing ReVGG-R2Net, a novel model ar...

Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification.

BACKGROUND: Magnetic Resonance Fingerprinting (MRF) is a technique that can provide rapid quantification of multiple tissue properties. Deep learning may potentially contribute to an accelerated acquisition of MRF. PURPOSE: (1) To develop a deep learning method to accelerate the acquisition for kidney MRF; (2) to evaluate its performance in healthy subjects and patients with renal masses. STUDY TY...

Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.

BACKGROUND: Histology images are a cornerstone of pathology, which allow automated analysis for disease diagnosis. However, variations in staining and...

Development and validation of a nomogram combining PI-RADS v2.1 and clinical indicators for the diagnosis of prostate cancer in patients with PSA ≤ 20 ng/mL.

PURPOSE: This investigation focused on developing a predictive clinical tool that combines biparametric MRI-derived PI-RADS v2.1 assessments with pati...

Deep learning enhanced label-free cervical screening via stimulated Raman cytology.

Cervical cancer screening remains pivotal for early detection and effective disease management, yet conventional cytopathological methods relying on s...

Predictive value of artificial intelligence-based quantitative CT feature analysis for diagnosing the pathological types of pulmonary nodules.

OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatm...

Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi.

Somatic evolution leads to clonal heterogeneity, which fuels cancer progression and therapy resistance. To decipher the consequences of clonal heterog...

Comparative analysis of convolutional neural network models for the histopathological differentiation of acinic cell carcinoma and secretory carcinoma.

OBJECTIVE: Although artificial intelligence tools show promise for enhancing the diagnosis of head and neck lesions, few studies have tested these res...

Consensus position statements for the standardized application of histological grading and staging systems in MASH clinical trials.

BACKGROUND & AIMS: Assessment of histologic disease activity (grading) and fibrosis (staging) is a prerequisite for patient selection and evaluation o...

Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?

Canine cutaneous mast cell tumors (ccMCTs) are frequent neoplasms with variable biological behaviors. Internal tandem duplication mutations in c-KIT e...

Enhancing the prediction accuracy of pathological downstaging in locally advanced rectal cancer using deep learning models with preoperative MRI and clinicopathological data.

PURPOSE: Conventional magnetic resonance imaging (MRI) for locally advanced rectal cancer (LARC) involves challenges in evaluating and predicting the ...

Cervical cancer diagnostics: non-coding RNAs and biosensors to AI-derived methods.

Cervical cancer ranks fourth in terms of cancer mortality among women. The most important risk factor for cervical cancer is infection with HPV 16 and...

Frequency-Domain Object Detection Network for Leukemia Diagnosis in Bone Marrow Microscopy.

Leukemia remains a prevalent hematologic malignancy, and its morphological heterogeneity presents challenges for reliable identification under optical...

Detection of Lymph Node Metastasis in Thyroid Cancer Using Deep Learning and Second Harmonic Generation Imaging.

Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, with a significant proportion of patients being susceptible to lymph n...

Extraction, preparation, pharmacological activities, and potential applications of ginsenosides Rk1 and Rg5.

ETHNOPHARMACOLOGICAL RELEVANCE: Panax species (Panax ginseng, Panax quinquefolius L., and Panax notoginseng) are globally recognized as valuable medic...

Public Preferences for Emerging Cancer Screening Modalities: A Systematic Review of Discrete Choice Experiments.

BACKGROUND: Early detection of cancer reduces mortality and morbidity, but conventional screening methods often face challenges such as invasiveness, ...

A deep learning-enabled toolkit for the 3D segmentation of ventricular cardiomyocytes.

Segmentation of cardiomyocytes in microscopic 3D volumes is key to our understanding of cardiac (patho-)physiology; however, it poses substantial expe...

Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [18F]FDG PET/CT sub-study of the Phase 3 GMMG-HD7 Trial.

PURPOSE: The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multi...

Real-Time Deep-Learning Image Reconstruction and Instrument Tracking in MR-Guided Biopsies.

BACKGROUND: Transrectal in-bore MR-guided biopsy (MRGB) is accurate but time-consuming, limiting clinical throughput. Faster imaging could improve wor...

Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.

BACKGROUND: Traumatic Brain Injury (TBI) is a global health concern, with mild TBI (mTBI) being the most common form. Despite its prevalence, accurate...