We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution (5160 WSIs) were used as the development set, while those from the other institution (5456 WSIs) were used as the unseen test set. Label distribution learning (LDL) was used to address a difference in label characteristics between the development and test sets. A combination of EfficientNet (a deep learning model) and LDL was utilized to develop an automatic prediction system. Quadratic weighted kappa (QWK) and accuracy in the test set were used as the evaluation metrics. The QWK and accuracy were compared between systems with and without LDL to evaluate the usefulness of LDL in system development. The QWK and accuracy were 0.364 and 0.407 in the systems with LDL and 0.240 and 0.247 in those without LDL, respectively. Thus, LDL improved the diagnostic performance of the automatic prediction system for the grading of histopathological images for cancer. By handling the difference in label characteristics using LDL, the diagnostic performance of the automatic prediction system could be improved for prostate cancer grading.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000939 | PMC |
| http://dx.doi.org/10.3390/cancers15051535 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.
J Magn Reson Imaging
January 2025
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is crucial for early proactive management and limit growing disease burden. The more recent advanced quantitative imaging techniques and deep learning (DL) algorithms in musculoskeletal imaging have shown great potential for visualizing "pre-OA.
View Article and Find Full Text PDFPredicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms.
Vet Med Sci
January 2025
Andırın Vocational School, Department of Computer Technologies, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Türkiye.
Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals.
View Article and Find Full Text PDFAutomated stenosis estimation of coronary angiographies using end-to-end learning.
Int J Cardiovasc Imaging
January 2025
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.
View Article and Find Full Text PDFDeep Learning Models for Automatic Classification of Anatomic Location in Abdominopelvic Digital Subtraction Angiography.
J Imaging Inform Med
January 2025
Department of Radiology, UC Davis School of Medicine, University of California, Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817-2307, USA.
Purpose: To explore the information in routine digital subtraction angiography (DSA) and evaluate deep learning algorithms for automated identification of anatomic location in DSA sequences.
Methods: DSA of the abdominal aorta, celiac, superior mesenteric, inferior mesenteric, and bilateral external iliac arteries was labeled with the anatomic location from retrospectively collected endovascular procedures performed between 2010 and 2020 at a tertiary care medical center. "Key" images within each sequence demonstrating the parent vessel and the first bifurcation were additionally labeled.
Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement.
Sci Rep
January 2025
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt.
The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!