Alle Papers – KI in der Krebserkennung
168.271 Papers insgesamt · Seite 353 von 400
Convolutional neural network for automated mass segmentation in mammography
Big data and artificial intelligence in cancer research
Weakly Supervised Biomedical Image Segmentation by Reiterative Learning
A statistical approach to texture description of medical images: a preliminary study
Triple assessment of breast lump.
Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images
Association of Mammographic Density with the Pathology of Subsequent Breast Cancer among Postmenopausal Women
Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks
Multimodal deep learning approaches for precision oncology: a comprehensive review
Melanoma Early Detection
Edge-Variational Graph Convolutional Networks for Uncertainty-Aware Disease Prediction
Update on Breast Density, Risk Estimation, and Supplemental Screening
CNL-UNet: A novel lightweight deep learning architecture for multimodal biomedical image segmentation with false output suppression
Segmentation and border identification of cells in images of peripheral blood smear slides
An ensemble classification approach for cervical cancer prediction using behavioral risk factors
Dental caries detection using a semi-supervised learning approach
An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique
Brain Tumor Classification Using MRI Images with K-Nearest Neighbor Method
Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images
Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology
Practicing Pathology in the Era of Big Data and Personalized Medicine
Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification
Image Analysis: Principles and Practice
AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images
Assessing mammographers' accuracy