Alle Papers – KI in der Krebserkennung

<b>Computer-Aided Detection (CAD) in Screening Mammography:</b> Sensitivity of Commercial CAD Systems for Detecting Architectural Distortion

Breast density implications and supplemental screening

Automated analysis of mammographic densities

Computer‐aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network

Cancer diagnosis in histopathological image: CNN based approach

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

Identification of High-Risk Patients Among Those Diagnosed With Thin Cutaneous Melanomas

Computed Medical Imaging

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer

Asymmetric Loss Functions and Deep Densely-Connected Networks for Highly-Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection

Computational Segmentation and Classification of Diabetic Glomerulosclerosis

Segmentation of breast ultrasound image with semantic classification of superpixels

Classification of newspaper image blocks using texture analysis

Mitotic Count and the Field of View Area

Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

AI for medical imaging goes deep

Digital pathology image analysis: opportunities and challenges

Pap-smear Benchmark Data For Pattern Classification

The Contributions of Breast Density and Common Genetic Variation to Breast Cancer Risk

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches