Alle Papers – KI in der Krebserkennung

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Some Practical Issues of Experimental Design and Data Analysis in Radiological ROC Studies

Variational image compression with a scale hyperprior

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

The Morphological Approach to Segmentation: The Watershed Transformation

Breast Cancer Diagnosis and Prognosis Via Linear Programming

Human–computer collaboration for skin cancer recognition

Evaluation metrics and statistical tests for machine learning

Deep learning in cancer diagnosis, prognosis and treatment selection

The use of receiver operating characteristic curves in biomedical informatics

Transfer learning for medical image classification: a literature review

Principles for Valid Histopathologic Scoring in Research

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Convolutional neural networks in medical image understanding: a survey

Medical image classification with convolutional neural network

Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study

Deep learning in histopathology: the path to the clinic

Breast Cancer Screening Using Tomosynthesis in Combination With Digital Mammography

A Review of Deep-Learning-Based Medical Image Segmentation Methods

COVID-19 Image Data Collection

Recurrent residual U-Net for medical image segmentation

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction

QuPath: Open source software for digital pathology image analysis