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Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. However, atypical mitosis classification remains challenging due to low prevalence, at times…

Atypical mitotic figures (AMFs) are important histopathological markers yet remain challenging to identify consistently, particularly under domain shift stemming from scanner, stain, and acquisition differences. We present a simple…

计算机视觉与模式识别 · 计算机科学 2025-08-29 Kaustubh Atey , Sameer Anand Jha , Gouranga Bala , Amit Sethi

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be…

计算机视觉与模式识别 · 计算机科学 2022-12-13 Marc Aubreville , Jonathan Ganz , Jonas Ammeling , Taryn A. Donovan , Rutger H. J. Fick , Katharina Breininger , Christof A. Bertram

Accurate mitotic figure classification is crucial in computational pathology, as mitotic activity informs cancer grading and patient prognosis. Distinguishing atypical mitotic figures (AMFs), which indicate higher tumor aggressiveness, from…

图像与视频处理 · 电气工程与系统科学 2025-09-04 Hana Feki , Alice Blondel , Thomas Walter

Mitotic figures (MFs) are relevant biomarkers in tumor grading. Differentiating atypical MFs (AMFs) from normal MFs (NMFs) remains difficult, as manual annotation is time-consuming and subjective. In this work an ensemble of ConvNeXtBase…

计算机视觉与模式识别 · 计算机科学 2025-08-29 Sara Krauss , Ellena Spieß , Daniel Hieber , Frank Kramer , Johannes Schobel , Dominik Müller

Mitotic activity is an important feature for grading several cancer types. Counting mitotic figures (MFs) is a time-consuming, laborious task prone to inter-observer variation. Inaccurate recognition of MFs can lead to incorrect grading and…

Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the…

计算机视觉与模式识别 · 计算机科学 2020-11-30 Marc Aubreville , Christof A. Bertram , Taryn A. Donovan , Christian Marzahl , Andreas Maier , Robert Klopfleisch

Mitotic figures are classified into typical and atypical variants, with atypical counts correlating strongly with tumor aggressiveness. Accurate differentiation is therefore essential for patient prognostication and resource allocation, yet…

图像与视频处理 · 电气工程与系统科学 2025-09-19 Mieko Ochi , Bae Yuan

This paper presents our solution for the MIDOG 2025 Challenge Track 2, which focuses on binary classification of normal mitotic figures (NMFs) versus atypical mitotic figures (AMFs) in histopathological images. Our approach leverages a…

计算机视觉与模式识别 · 计算机科学 2025-08-27 Yosuke Yamagishi , Shouhei Hanaoka

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis…

Each woman living in the United States has about 1 in 8 chance of developing invasive breast cancer. The mitotic cell count is one of the most common tests to assess the aggressiveness or grade of breast cancer. In this prognosis,…

计算机视觉与模式识别 · 计算机科学 2022-02-18 Robin Elizabeth Yancey

Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate…

计算机视觉与模式识别 · 计算机科学 2018-07-06 Siddhant Rao

Atypical mitotic figures (AMFs) represent abnormal cell division associated with poor prognosis. Yet their detection remains difficult due to low prevalence, subtle morphology, and inter-observer variability. The MIDOG 2025 challenge…

图像与视频处理 · 电气工程与系统科学 2025-10-15 Guillaume Balezo , Hana Feki , Raphaël Bourgade , Lily Monnier , Matthieu Blons , Alice Blondel , Etienne Decencière , Albert Pla Planas , Thomas Walter

Atypical mitotic figures are important biomarkers of tumor aggressiveness in histopathology, yet reliable recognition remains challenging due to severe class imbalance and variability across imaging domains. We present a DenseNet-121-based…

计算机视觉与模式识别 · 计算机科学 2025-11-04 Adinath Dukre , Ankan Deria , Yutong Xie , Imran Razzak

Motivation: Accurate classification of mitotic figures into normal and atypical types is crucial for tumor prognostication in digital pathology. However, developing robust deep learning models for this task is challenging due to the subtle…

We tackle atypical versus normal mitosis classification in the MIDOG 2025 challenge using EfficientViT-L2, a hybrid CNN--ViT architecture optimized for accuracy and efficiency. A unified dataset of 13,938 nuclei from seven cancer types…

图像与视频处理 · 电气工程与系统科学 2025-09-04 Xuan Qi , Dominic Labella , Thomas Sanford , Maxwell Lee

Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle…

计算机视觉与模式识别 · 计算机科学 2021-08-23 Walt Williams , Jimmy Hall

Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammography, pretrained on the largest and most diverse dataset to date - 140,677 patients…

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 subject to a strong…

Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns observable in histopathology sections from tumours are…

图像与视频处理 · 电气工程与系统科学 2020-09-21 Yinxi Wang , Kimmo Kartasalo , Masi Valkonen , Christer Larsson , Pekka Ruusuvuori , Johan Hartman , Mattias Rantalainen
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