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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.
Structure-preserving color normalization and sparse stain separation for histological images
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Breast carcinoma malignancy grading by Bloom-Richardson system vs proliferation index: Reproducibility of grade and advantages of proliferation index
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Proliferation is the strongest prognosticator in node-negative breast cancer: significance, error sources, alternatives and comparison with molecular prognostic markers
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Mitotic index of invasive breast carcinoma. Achieving clinically meaningful precision and evaluating tertial cutoffs
Meyer, J.S., Cosatto, E., Graf, H.P., 2009 · 2009
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Hallgren, K.A., 2012 · 2012
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Mitos & atypia
Roux, L., Racoceanu, D., Capron, F., Calvo, J., Attieh, E., Le Naour, G., Gloaguen, A., 2014 · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J., 2015 · 2015
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Assessment of algorithms for mitosis detection in breast cancer histopathology images
Veta, M., Van Diest, P.J., Willems, S.M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A.B., Vestergaard, J.S., Dahl, A.B., et al., 2015 · 2015
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Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
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The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D.N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W.K., Ohgaki, H., Wiestler, O.D., Kleihues, P., Ellison, D.W., 2016 · 2016
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Mitosis counting in breast cancer: Object-level interobserver agreement and comparison to an automatic method
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Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017 · 2017
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Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A., 2017 · 2017
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Focal loss for dense object detection, in: Proceedings of the IEEE international conference on computer vision, pp. 2980–2988
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P., 2017 · 2017
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Cascade r-cnn: Delving into high quality object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154–6162
Cai, Z., Vasconcelos, N., 2018 · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8789–8797
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J., 2018 · 2018
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Deep learning assisted mitotic counting for breast cancer
Balkenhol, M.C., Tellez, D., Vreuls, W., Clahsen, P.C., Pinckaers, H., Ciompi, F., Bult, P., van der Laak, J.A., 2019 · 2019
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A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor
Bertram, C.A., Aubreville, M., Marzahl, C., Maier, A., Klopfleisch, R., 2019 · 2019
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Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Domain-robust mitotic figure detection with style transfer, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 23–31
Chung, Y., Cho, J., Park, J., 2022 · 2021
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MitoDet: Simple and robust mitosis detection, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 53–57
Dexl, J., Benz, M., Bruns, V., Kuritcy, P., Wittenberg, T., 2022 · 2021
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Mitotic figures—normal, atypical, and imposters: A guide to identification
Donovan, T.A., Moore, F.M., Bertram, C.A., Luong, R., Bolfa, P., Klopfleisch, R., Tvedten, H., Salas, E.N., Whitley, D.B., Aubreville, M., et al., 2021 · 2021
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Domain-specific cycle-gan augmentation improves domain generalizability for mitosis detection, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 40–47
Fick, R.H., Moshayedi, A., Roy, G., Dedieu, J., Petit, S., Hadj, S.B., 2022 · 2021
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Graham, S., Vu, Q.D., Raza, S.E.A., Azam, A., Tsang, Y.W., Kwak, J.T., Rajpoot, N., 2019 · 2019
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Deep learning-based gleason grading of prostate cancer from histopathology images—role of multiscale decision aggregation and data augmentation
Karimi, D., Nir, G., Fazli, L., Black, P.C., Goldenberg, L., Salcudean, S.E., 2019 · 2019
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Learning domain-invariant representations of histological images
Lafarge, M.W., Pluim, J.P., Eppenhof, K.A., Veta, M., 2019 · 2019
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Rise of the machines: advances in deep learning for cancer diagnosis
Levine, A.B., Schlosser, C., Grewal, J., Coope, R., Jones, S.J., Yip, S., 2019 · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks, in: International Conference on Machine Learning, PMLR. pp. 6105–6114
Tan, M., Le, Q., 2019 · 2019
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Predicting breast tumor proliferation from whole-slide images: the tupac16 challenge
Veta, M., Heng, Y.J., Stathonikos, N., Bejnordi, B.E., Beca, F., Wollmann, T., Rohr, K., Shah, M.A., Wang, D., Rousson, M., et al., 2019 · 2019
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Nuclick: a deep learning framework for interactive segmentation of microscopic images
Koohbanani, N.A., Jahanifar, M., Tajadin, N.Z., Rajpoot, N., 2020 · 2020
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Deep learning-based quantification of pulmonary hemosiderophages in cytology slides
Marzahl, C., Aubreville, M., Bertram, C.A., Stayt, J., Jasensky, A.K., Bartenschlager, F., Fragoso-Garcia, M., Barton, A.K., Elsemann, S., Jabari, S., et al., 2020 · 2020
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Stain-robust mitotic figure detection for the Mitosis Domain Generalization Challenge, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 48–52
Jahanifar, M., Shepard, A., Zamanitajeddin, N., Bashir, R.S., Bilal, M., Khurram, S.A., Minhas, F., Rajpoot, N., 2022 · 2021
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Robust interactive semantic segmentation of pathology images with minimal user input, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 674–683
Jahanifar, M., Tajeddin, N.Z., Koohbanani, N.A., Rajpoot, N.M., 2021 · 2021
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Multi-source domain adaptation using gradient reversal layer for mitotic cell detection, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 58–61
Kondo, S., 2022 · 2021
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Rotation invariance and extensive data augmentation: a strategy for the MItosis DOmain Generalization (MIDOG) Challenge, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 62–67
Lafarge, M., Koelzer, V., 2022 · 2021
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Roto-translation equivariant convolutional networks: Application to histopathology image analysis
Lafarge, M.W., Bekkers, E.J., Pluim, J.P., Duits, R., Veta, M., 2021 · 2021
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Detecting mitosis against domain shift using a fused detector and deep ensemble classification model for MIDOG challenge, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 68–72
Liang, J., Wang, C., Cheng, Y., Wang, Z., Wang, F., Huang, L., Yu, Z., Wang, Y., 2022 · 2021
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Domain adaptive cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 73–76
Long, X., Cheng, Y., Mu, X., Liu, L., Liu, J., 2022 · 2021
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Two-step domain adaptation for mitosis cell detection in histopathology images, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 32–39
Nateghi, R., Pourakpour, F., 2022 · 2021
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An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites
Pasqualino, G., Furnari, A., Signorello, G., Farinella, G.M., 2021 · 2021
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Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10213–10224
Qiao, S., Chen, L.C., Yuille, A., 2021 · 2021
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Cascade R-CNN for MIDOG challenge, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 81–85
Razavi, S., Dambandkhameneh, F., Androutsos, D., Done, S., Khademi, A., 2022 · 2021
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Sk-unet: An improved u-net model with selective kernel for the segmentation of lge cardiac mr images
Wang, X., Yang, S., Fang, Y., Wei, Y., Wang, M., Zhang, J., Han, X., 2021 · 2021
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Sk-unet model with fourier domain for mitosis detection, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis, MICCAI 2021 Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 86–90
Yang, S., Luo, F., Zhang, J., Wang, X., 2022 · 2021
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Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis
Aubreville, M., Zimmerer, D., Heinrich, M. (Eds.), 2022 · 2022
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Domain adversarial retinanet as a reference algorithm for the midog challenge, in: Biomedical Image Registration, Domain Generalization and Out-of-Distribution Analysis: The MICCAI Challenges L2R, MIDOG and MOOD, Springer, Cham. pp. 5–13
Wilm, F., Marzahl, C., Breininger, K., Aubreville, M., 2022 · 2022
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V., 2016 · 2030
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