Alle Papers – KI in der Krebserkennung

Parallax and mass-ratio of eta CAS from photographs taken with the 24-inch Sproul refractor.

Survival outcome prediction in cervical cancer: Cox models vs deep-learning model

Guest editorial computer-aided diagnosis in medical imaging

Multiclass cancer classification and biomarker discovery using GA-based algorithms

Mammographic density and risk of breast cancer by age and tumor characteristics

Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Converting tabular data into images for deep learning with convolutional neural networks

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks

Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images

Channel Interaction Networks for Fine-Grained Image Categorization

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Improvement of mammographic mass characterization using spiculation measures and morphological features

How well do physicians recognize melanoma and other problem lesions?

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Machine learning applications in prostate cancer magnetic resonance imaging

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning

Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Sonographic Detection and Sonographically Guided Biopsy of Breast Microcalcifications

Cost-Effectiveness of Digital Mammography Breast Cancer Screening

Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

The “Laboratory” Effect: Comparing Radiologists' Performance and Variability during Prospective Clinical and Laboratory Mammography Interpretations