Mitosis Detection
12 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation
Our main algorithmic choices are as follows: first, to enhance the generalizability of our detector and classification networks, we use a state-of-the-art residual Cycle-GAN to transform each scanner domain to every other scanner domain.
Deep Learning-based mitosis detection in breast cancer histologic samples
This is the submission for mitosis detection in the context of the MIDOG 2021 challenge.
Cascade RCNN for MIDOG Challenge
Mitotic counts are one of the key indicators of breast cancer prognosis.
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge
Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition.
MitoDet: Simple and robust mitosis detection
Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions.
Mitosis Detection for Breast Cancer Pathology Images Using UV-Net
The difficulty of detecting mitosis and its similarity to non-mitosis objects has remained a challenge in computational pathology.
Sk-Unet Model with Fourier Domain for Mitosis Detection
Mitotic count is the most important morphological feature of breast cancer grading.
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge
We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images.
Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images
We propose a two-step domain shift-invariant mitosis cell detection method based on Faster RCNN and a convolutional neural network (CNN).
Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations
Usually, incomplete annotations can be achieved, where positive labeling results are carefully examined to ensure their reliability but there can be other positive instances, i. e., cells of interest, that are not included in the annotations.
