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Mitosis Detection

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Showing 26–47 of 47 papers

TitleStatusHype
Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation—0
Robust Multi-Domain Mitosis Detection—0
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge—0
Sk-Unet Model with Fourier Domain for Mitosis Detection—0
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures—0
Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies—0
Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images—0
Virtual staining for mitosis detection in Breast Histopathology—0
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks—0
Semi-supervised estimation of event temporal length for cell event detection—0
Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas—0
Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases—0
Assessment of algorithms for mitosis detection in breast cancer histopathology images—0
Cascade RCNN for MIDOG Challenge—0
Challenging mitosis detection algorithms: Global labels allow centroid localization—0
Deep Learning-based mitosis detection in breast cancer histologic samples—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge—0
Domain-adversarial neural networks to address the appearance variability of histopathology images—0
Evaluation of the mitotic score of invasive breast carcinomas on digital slide: development and contribution of a mitosis detection algorithm—0
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset—0
Improving mitosis detection on histopathology images using large vision-language models—0
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