Alle Papers – KI in der Krebserkennung

Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection

MMS-Net: Multi-level multi-scale feature extraction network for medical image segmentation

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Image pre-processing in computer vision systems for melanoma detection

Mammogram Breast Cancer Image Detection Using Image Processing Functions

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment

Micro–Computed Tomography–Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography

A genetic algorithm‐based method for optimizing the performance of a computer‐aided diagnosis scheme for detection of clustered microcalcifications in mammograms

Classification of Benign and Malignant Thyroid Nodules Using Wavelet Texture Analysis of Sonograms

An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms

Radial symmetries based decomposition of cell clusters in binary and gray level images

Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography

Computer-aided diagnostic models in breast cancer screening

Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection

Medical Image Understanding and Analysis

Ultrasound-guided fine needle aspiration cytology as an addendum to sentinel lymph node biopsy can perfect the staging strategy in melanoma patients

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images

Digital Imagery/Telecytology

Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines

Inter-observer and intra-observer variability of mammogram interpretation: a field study