MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images

Sebai, Meriem; Wang, Xinggang; Wang, Tianjiang

doi:10.1007/s11517-020-02175-z

Cited by 50 publications

(20 citation statements)

References 47 publications

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“…Table 8, shows the comparison of the designed MS-RCNN model with state-of-the-art methods on the ICPR 2012 test set in terms of Precision, Recall, and F score. Our approach significantly outperforms competing techniques, achieving highest F score of 92% on ICPR 2012 test dataset and provides at least 5% improvement in F score over the highest value recently reported by Sebai et al [11]).…”

Section: ) the Effectiveness Of Input Image Size And Super-resolved supporting

confidence: 48%

“…For the sake of fair comparison, we checked performance on ICPR 2014 and AMIDA13 test sets. Performance comparison with competing methods and two recently proposed state-of-the-art methods (DeepMitosis [7] & MaskMitosis [11]) on ICPR 2014 test data, is given in Table 10. In comparison to other methods, our SmallMitosis yields the best results with F score of 0.495 on ICPR 2014 test data.…”

Section: ) Performance Comparison With State-of-the-art Methods On Wmentioning

confidence: 99%

“…Further, Sebai et al [11] applied a multi task deep learning framework called Mask RCNN for detection and instance segmentation of mitosis regions. In [11], fully annotated mitosis dataset is used for mitosis localization and classification while a weakly annotated dataset is used for segmentation and estimation of mitosis mask labels. This method achieved F score of 0.863 and 0.475 on the ICPR 2012 and ICPR 2014 datasets respectively.…”

Section: Introductionmentioning

confidence: 99%

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SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

et al. 2021

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Section: ) the Effectiveness Of Input Image Size And Super-resolved supporting

confidence: 48%

Section: ) Performance Comparison With State-of-the-art Methods On Wmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

et al. 2021

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“…Another interesting area is that of histopathological images, which are progressively digitized. Several papers have been published in this field [284][285][286][287][288][289][290]. Human pathologists read these images laboriously; they search for malignancy markers, such as a high index of cell proliferation, using molecular markers (e.g.…”

Section: Detectionmentioning

confidence: 99%

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

et al. 2021

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In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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“…Later, several other CNN based methods have published. e DeepMitosis [21] and MaskMitosis [37] yield a good performance on the [21] 0.431 0.443 0.437 CasNN [17] 0.411 0.478 0.442 MaskMitosis [37] 0.500 0.453 0.475 LRCNN + in group [38] 0.654 0.663 0.659 Efficient mitosis detection [39] 0.534 0.661 0.585 SegMitos-r15R30 [5] 0.594 0.512 0.550 SegMitos-random [5] 0 e CasNN [17] approach requires two different networks: one is used to retrieve the mitosis candidates, and the other is used to classify the candidates, leading to less accurate detection.…”

Section: Discussionmentioning

confidence: 99%

Representation of Differential Learning Method for Mitosis Detection

Ali

Retta

et al. 2021

Journal of Healthcare Engineering

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The breast cancer microscopy images acquire information about the patient’s ailment, and the automated mitotic cell detection outcomes have generally been utilized to ease the massive amount of pathologist’s work and help the pathologists make clinical decisions quickly. Several previous methods were introduced to solve automated mitotic cell count problems. However, they failed to differentiate between mitotic and nonmitotic cells and come up with an imbalance problem, which affects the performance. This paper proposes a Representation Differential Learning Method (RDLM) for mitosis detection through deep learning to detect the accurate mitotic cell area on pathological images. Our proposed method has been divided into two parts: Global bank Feature Pyramid Network (GLB-FPN) and focal loss (FL). The GLB feature fusion method with FPN essentially makes the encoder-decoder pay attention, to further extract the region of interest (ROIs) for mitotic cells. On this basis, we extend the GLB-FPN with a focal loss to mitigate the data imbalance problem during the training stage. Extensive experiments have shown that RDLM significantly outperforms on visualization view and achieves the best performance in quantitative matrices than other proposed approaches on the MITOS-ATYPIA-14 contest dataset. Our framework reaches a 0.692 F1-score. Additionally, RDLM achieves 5% improvements than GLB with FPN in F1-score on the mitosis detection task.

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MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images

Cited by 50 publications

References 47 publications

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

SmallMitosis: Small Size Mitotic Cells Detection in Breast Histopathology Images

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Representation of Differential Learning Method for Mitosis Detection

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