Alle Papers – KI in der Krebserkennung

Unifying neural learning and symbolic reasoning for spinal medical report generation

Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Deep learning for skin lesion segmentation

Using BI-RADS Stratifications as Auxiliary Information for Breast Masses Classification in Ultrasound Images

ECM-aware cell-graph mining for bone tissue modeling and classification

Optical Markers in Duodenal Mucosa Predict the Presence of Pancreatic Cancer

Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images

Expertise in clinical pathology: combining the visual and cognitive perspective

Performance of a Previously Validated CT Colonography Computer-Aided Detection System in a New Patient Population

Artificial intelligence and its potential in oncology

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

Gan-based data augmentation to improve breast ultrasound and mammography mass classification

A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced Faster R-CNN

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

The Reliability of a Cytological Prediction of Cervical Adenocarcinoma In Situ

Harnessing artificial intelligence for prostate cancer management

Augmenting diagnostic vision with AI

Quantitative Ultrasound Analysis for Classification of BI-RADS Category 3 Breast Masses

Breast cancer detection using machine learning approaches: a comparative study

Comprehensive and Comparative Global and Local Feature Extraction Framework for Lung Cancer Detection Using CT Scan Images

Anomaly Detection for Skin Disease Images Using Variational Autoencoder

Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams

ERDUnet: An Efficient Residual Double-Coding Unet for Medical Image Segmentation

Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer