Mitosis Detection
12 papers with code • 0 benchmarks • 0 datasets
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Latest papers
Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP).
Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
First, we generate an image pair not containing mitosis events by frame-order flipping.
A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
antifen/mitosis-nuclei-detection
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In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible.
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
MEDAL-IITB/MIDOG
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The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade.
ReCasNet: Improving consistency within the two-stage mitosis detection framework
Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences.
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
scjjb/midog_domain_adaptation
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Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods.
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.
Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN
In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN.
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
tueimage/se2cnn
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This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
mikevoets/jama16-retina-replication
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We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22).
