Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974535 | PMC |
| http://dx.doi.org/10.1187/cbe.24-07-0185 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A histopathology image dataset for Johne's disease detection and research.
Data Brief
October 2025
Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030 Irbid 22110 Jordan.
Johne's disease (paratuberculosis), caused by subspecies , is a chronic intestinal infection that affects ruminants and poses significant challenges for livestock health and management. Accurate and early diagnosis is of paramount importance for effective disease control, yet traditional histopathological assessment requires expert interpretation and remains subject to interobserver variability. In this paper, we present a curated dataset of histopathological slide images collected from tissue samples confirmed to be positive or negative for Johne's disease.
View Article and Find Full Text PDFDifferentiation of benign and malignant oral lesions through surface texture analysis and SVM modeling.
Clin Oral Investig
September 2025
Department of Oral & Maxillofacial Radiology, Faculty of Dentistry, Ege University, Bornova, İzmir, Türkiye.
Objectives: To evaluate the diagnostic potential of surface texture features extracted from clinical images in objectively differentiating benign from malignant oral lesions, and to validate classification performance of a Support Vector Machine (SVM) model using these features.
Materials And Methods: This study included 275 intraoral photographs of oral mucosal lesions with biopsy-confirmed diagnoses, sourced from both institutional archives and a public dataset. Lesion areas were manually annotated and converted into 3D surface plots to extract grayscale-based texture features.
Cascaded Self-Supervision to Advance Cardiac MRI Segmentation in Low-Data Regimes.
Bioengineering (Basel)
August 2025
BioTechMed-Graz, 8010 Graz, Austria.
Deep learning has shown remarkable success in medical image analysis over the last decade; however, many contributions focused on supervised methods which learn exclusively from labeled training samples. Acquiring expert-level annotations in large quantities is time-consuming and costly, even more so in medical image segmentation, where annotations are required on a pixel level and often in 3D. As a result, available labeled training data and consequently performance is often limited.
View Article and Find Full Text PDFUncertainty-Aware Cross-Training for Semi-Supervised Medical Image Segmentation.
IEEE Trans Image Process
September 2025
Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mean-teacher (MT) based semi-supervised methods utilize consistency regularization to effectively leverage valuable information from unlabeled data. However, these methods often heavily rely on the student model and overlook the potential impact of cognitive biases within the model.
View Article and Find Full Text PDFRevealing genetic drivers of ovarian cancer and chemoresistance: insights from whole-genome CRISPR-knockout library screens.
Cell Oncol (Dordr)
August 2025
Translational Oncology Group, School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW, Australia.
Understanding genetic dependencies in cancer is key to identifying novel actionable drug targets to advance precision medicine. Whole-genome CRISPR-knockout library screening methods have facilitated this goal. Pooled libraries of single guide RNAs (sgRNAs) targeting over 90% of the annotated protein coding genome are used to induce gene knockouts in pre-clinical cancer models.
View Article and Find Full Text PDF