Papers Mitosis Detection
“Mitosis Detection” 태그가 달린 논문 47편 · 필터 해제
Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas
Z-stack scanning is an emerging whole slide imaging technology that captures multiple focal planes alongside the z-axis of a glass slide. Because z-stacking can offer enhanced depth information compared to the single-lay…
Mitosis Detectionwhole slide imagesSupporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies
The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consumi…
Mitosis DetectionEvaluation of the mitotic score of invasive breast carcinomas on digital slide: development and contribution of a mitosis detection algorithm
Introduction: Nottingham grading system is a major prognostic factor for invasive breast carcinoma (IBC). Its determination requires the evaluation of the mitotic score (MS) which is subject to low intra- and inter-obser…
Mitosis Detectionwhole slide imagesImproving mitosis detection on histopathology images using large vision-language models
In certain types of cancerous tissue, mitotic count has been shown to be associated with tumor proliferation, poor prognosis, and therapeutic resistance. Due to the high inter-rater variability of mitotic counting by pat…
Domain GeneralizationImage CaptioningMitosis DetectionPrognosis+4Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
Mitosis detection is one of the fundamental tasks in computational pathology, which is extremely challenging due to the heterogeneity of mitotic cell. Most of the current studies solve the heterogeneity in the technical …
DiversityMitosis DetectionMitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
Detection of mitosis events plays an important role in biomedical research. Deep-learning-based mitosis detection methods have achieved outstanding performance with a certain amount of labeled data. However, these method…
Dataset GenerationMitosis DetectionA novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists …
Mitosis DetectionFine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset
Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (M…
Data AugmentationDomain GeneralizationMitosis DetectionChallenging mitosis detection algorithms: Global labels allow centroid localization
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, …
Mitosis DetectionPrognosisImproving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysis often fails on unseen patient data due…
Mitosis DetectionPrognosisMulti tasks RetinaNet for mitosis detection
The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, al…
Cell DetectionData AugmentationDomain GeneralizationMitosis DetectionMitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic fig…
Domain GeneralizationMitosis Detectionwhole slide imagesDetecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge
This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN). Our method consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract p…
Active LearningData AugmentationMitosis DetectionMitosis domain generalization in histopathology images -- The MIDOG challenge
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be…
Domain GeneralizationMitosis DetectionReCasNet: Improving consistency within the two-stage mitosis detection framework
Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. The…
Cell DetectionDeep LearningMitosis Detectionobject-detection+2Robust Multi-Domain Mitosis Detection
Domain variability is a common bottle neck in developing generalisable algorithms for various medical applications. Motivated by the observation that the domain variability of the medical images is to some extent compact…
Image-to-Image TranslationMitosis DetectionTranslationRobust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation
For the MIDOG mitosis detection challenge, we created a cascade algorithm consisting of a Mask-RCNN detector, followed by a classification ensemble consisting of ResNet50 and DenseNet201 to refine detected mitotic candid…
Data AugmentationMitosis DetectionDeep Learning-based mitosis detection in breast cancer histologic samples
This is the submission for mitosis detection in the context of the MIDOG 2021 challenge. It is based on the two-stage objection model Faster RCNN as well as DenseNet as a backbone for the neural network architecture. It …
Deep LearningMitosis DetectionCascade RCNN for MIDOG Challenge
Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are…
Mitosis DetectionPrognosisRotation 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. The purpose …
Data AugmentationDomain GeneralizationMitosis Detection