Alle Papers – KI in der Krebserkennung

Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Multi-class texture analysis in colorectal cancer histology

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

A methodology for evaluation of boundary detection algorithms on medical images

Approaches for automated detection and classification of masses in mammograms

Medical Image Segmentation based on U-Net: A Review

Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

Evolving support vector machines using fruit fly optimization for medical data classification

ImageJ: Image processing and analysis in Java

A whole-slide foundation model for digital pathology from real-world data

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Big Data Application in Biomedical Research and Health Care: A Literature Review

Guideline implementation for breast healthcare in low-income and middle-income countries

Digital pathology and artificial intelligence in translational medicine and clinical practice

A visual–language foundation model for pathology image analysis using medical Twitter

Towards a guideline for evaluation metrics in medical image segmentation

FAT-Net: Feature adaptive transformers for automated skin lesion segmentation

Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine

Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios