Alle Papers – KI in der Krebserkennung

Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

Contemporary Diagnostic Imaging Modalities for the Staging and Surveillance of Melanoma Patients: a Meta-analysis

Attention-based VGG-16 model for COVID-19 chest X-ray image classification

Novelty detection for the identification of masses in mammograms

Deep Learning–Based Histopathologic Assessment of Kidney Tissue

Mammographic density, breast cancer risk and risk prediction

Breast cancer histopathological image classification using a hybrid deep neural network

Mixed Transformer U-Net for Medical Image Segmentation

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

Machine learning approaches in medical image analysis: From detection to diagnosis

Distributed deep learning networks among institutions for medical imaging

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

SVM and SVM Ensembles in Breast Cancer Prediction

ACR Appropriateness Criteria Breast Cancer Screening

Artificial intelligence for clinical oncology

Segmentation, registration, and measurement of shape variation via image object shape

Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms

Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings

Mammographic Breast Density: Impact on Breast Cancer Risk and Implications for Screening

Computer‐aided diagnosis in the era of deep learning

The Association of Measured Breast Tissue Characteristics with Mammographic Density and Other Risk Factors for Breast Cancer

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

A deep learning approach for the analysis of masses in mammograms with minimal user intervention