Alle Papers – KI in der Krebserkennung

Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer

Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set

Image-guided breast biopsy and localisation: recommendations for information to women and referring physicians by the European Society of Breast Imaging

A comparative study on thyroid disease diagnosis using neural networks

Exploration of machine learning techniques in predicting multiple sclerosis disease course

A hybrid algorithm for lung cancer classification using SVM and Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Construction of a Bayesian network for mammographic diagnosis of breast cancer

Diagnostic accuracy of stereotactic large‐core needle biopsy for nonpalpable breast disease: Results of a multicenter prospective study with 95% surgical confirmation

One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

Automatic mass detection in mammograms using deep convolutional neural networks

Accuracy of Digital Breast Tomosynthesis for Depicting Breast Cancer Subgroups in a UK Retrospective Reading Study (TOMMY Trial)

A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis

A Novel Digital Score for Abundance of Tumour Infiltrating Lymphocytes Predicts Disease Free Survival in Oral Squamous Cell Carcinoma

Multimodal Deep Learning for Cervical Dysplasia Diagnosis

Overview of object oriented data analysis

Validation of a digital pathology system including remote review during the COVID-19 pandemic

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation

RescueNet: An unpaired GAN for brain tumor segmentation

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

Digital Processing of Biomedical Images

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma