Alle Papers – KI in der Krebserkennung

Effect of Baseline Breast Density on Breast Cancer Incidence, Stage, Mortality, and Screening Parameters: 25-Year Follow-up of a Swedish Mammographic Screening

<b>Improvement in Sensitivity of Screening Mammography with Computer-Aided Detection:</b>A Multiinstitutional Trial

Quick‐and‐clean article figures with FigureJ

Standardization of Gleason grading among 337 European pathologists

Convolutional neural networks for breast cancer detection in mammography: A survey

Ambiguous Medical Image Segmentation Using Diffusion Models

Mammographic image analysis

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies

Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models

Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

A Survey on Medical Image Segmentation

Segmentation and Quantitative Analysis of Epithelial Tissues

Accuracy of Self-Report of Mammography and Pap Smear in a Low-Income Urban Population

Computer-aided Detection with Screening Mammography in a University Hospital Setting

CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking

Time Course of Perception and Decision Making During Mammographic Interpretation

Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach

Data Augmentation for Skin Lesion Analysis

Mammographic Breast Density and Race

Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning

An analytical method for diseases prediction using machine learning techniques

Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis