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Point-based cell detection (PCD), which pursues high-performance cell sensing under low-cost data annotation, has garnered increased attention in computational pathology community.
Self-supervised similarity learning for digital pathology
Gildenblat, J.; and Klaiman, E. 2019 · 1905
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A generalized loss function for crowd counting and localization
Wan, J.; Liu, Z.; and Chan, A. B. 2021 · 1983
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Improved baselines with momentum contrastive learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2020b · 2003
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2020 · 2010
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K.; Ledig, C.; Newcombe, V. F.; Simpson, J. P.; Kane, A. D.; Menon, D. K.; Rueckert, D.; and Glocker, B. 2017 · 2017
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Feature pyramid networks for object detection
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; and Belongie, S. 2017 · 2017
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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C.; Zhu, Y.; Papandreou, G.; Schroff, F.; and Adam, H. 2018 · 2018
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The cancer genome atlas: creating lasting value beyond its data
Hutter, C.; and Zenklusen, J. C. 2018 · 2018
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Sfcn-opi: Detection and fine-grained classification of nuclei using sibling fcn with objectness prior interaction
Zhou, Y.; Dou, Q.; Chen, H.; Qin, J.; and Heng, P.-A. 2018 · 2018
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Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Graham, S.; Vu, Q. D.; Raza, S. E. A.; Azam, A.; Tsang, Y. W.; Kwak, J. T.; and Rajpoot, N. 2019 · 2019
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Exploring the prognostic value of the neutrophil-to-lymphocyte ratio in cancer
Howard, R.; Kanetsky, P. A.; and Egan, K. M. 2019 · 2019
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Density map regression guided detection network for rgb-d crowd counting and localization
Lian, D.; Li, J.; Zheng, J.; Luo, W.; and Gao, S. 2019 · 2019
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Adaptive weighting multi-field-of-view CNN for semantic segmentation in pathology
Tokunaga, H.; Teramoto, Y.; Yoshizawa, A.; and Bise, R. 2019 · 2019
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Multi-field of view aggregation and context encoding for single-stage nucleus recognition
Bai, T.; Xu, J.; and Xing, F. 2020 · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M.; Misra, I.; Mairal, J.; Goyal, P.; Bojanowski, P.; and Joulin, A. 2020 · 2020
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Vitae: Vision transformer advanced by exploring intrinsic inductive bias
Xu, Y.; Zhang, Q.; Zhang, J.; and Tao, D. 2021 · 2021
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Context-aware learning for cancer cell nucleus recognition in pathology images
Bai, T.; Xu, J.; Zhang, Z.; Guo, S.; and Luo, X. 2022 · 2022
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Artificial intelligence-assisted score analysis for predicting the expression of the immunotherapy biomarker PD-L1 in lung cancer
Cheng, G.; Zhang, F.; Xing, Y.; Hu, X.; Zhang, H.; Chen, S.; Li, M.; Peng, C.; Ding, G.; Zhang, D.; et al. 2022 · 2022
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Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
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An end-to-end transformer model for crowd localization
Liang, D.; Xu, W.; and Bai, X. 2022 · 2022
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Huang, Z.; Ding, Y.; Song, G.; Wang, L.; Geng, R.; He, H.; Du, S.; Liu, X.; Tian, Y.; Liang, Y.; et al. 2020 · 2020
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Generalizing nucleus recognition model in multi-source ki67 immunohistochemistry stained images via domain-specific pruning
Cai, J.; Zhu, C.; Cui, C.; Li, H.; Wu, T.; Zhang, S.; and Yang, L. 2021 · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Caron, M.; Touvron, H.; Misra, I.; Jégou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021 · 2021
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Deep multi-magnification networks for multi-class breast cancer image segmentation
Ho, D. J.; Yarlagadda, D. V.; D’Alfonso, T. M.; Hanna, M. G.; Grabenstetter, A.; Ntiamoah, P.; Brogi, E.; Tan, L. K.; and Fuchs, T. J. 2021 · 2021
Cited alongside, same era.
Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images
Jiao, Y.; Li, J.; Qian, C.; and Fei, S. 2021 · 2021
Cited alongside, same era.
Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture
Schmitz, R.; Madesta, F.; Nielsen, M.; Krause, J.; Steurer, S.; Werner, R.; and Rösch, T. 2021 · 2021
Cited alongside, same era.
Rethinking counting and localization in crowds: A purely point-based framework
Song, Q.; Wang, C.; Jiang, Z.; Wang, Y.; Tai, Y.; Wang, C.; Li, J.; Huang, F.; and Wu, Y. 2021 · 2021
Cited alongside, same era.
Focal inverse distance transform maps for crowd localization
Liang, D.; Xu, W.; Zhu, Y.; and Zhou, Y. 2022 · 2022
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Boosting Crowd Counting via Multifaceted Attention
Lin, H.; Ma, Z.; Ji, R.; Wang, Y.; and Hong, X. 2022 · 2022
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What makes transfer learning work for medical images: feature reuse & other factors
Matsoukas, C.; Haslum, J. F.; Sorkhei, M.; Söderberg, M.; and Smith, K. 2022 · 2022
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End-to-end cell recognition by point annotation
Shui, Z.; Zhang, S.; Zhu, C.; Wang, B.; Chen, P.; Zheng, S.; and Yang, L. 2022 · 2022
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Weakly supervised learning for cell recognition in immunohistochemical cytoplasm staining images
Zhang, S.; Zhu, C.; Li, H.; Cai, J.; and Yang, L. 2022b · 2022
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Self pre-training with masked autoencoders for medical image analysis
Zhou, L.; Liu, H.; Bae, J.; He, J.; Samaras, D.; and Prasanna, P. 2022 · 2022
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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Kang, M.; Song, H.; Park, S.; Yoo, D.; and Pereira, S. 2023 · 2023
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Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification
Li, H.; Zhu, C.; Zhang, Y.; Sun, Y.; Shui, Z.; Kuang, W.; Zheng, S.; and Yang, L. 2023 · 2023
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Optimal Transport Minimization: Crowd Localization on Density Maps for Semi-Supervised Counting
Lin, W.; and Chan, A. B. 2023 · 2023
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What Do Self-Supervised Vision Transformers Learn?
Park, N.; Kim, W.; Heo, B.; Kim, T.; and Yun, S. 2023 · 2023
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OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Ryu, J.; Puche, A. V.; Shin, J.; Park, S.; Brattoli, B.; Lee, J.; Jung, W.; Cho, S. I.; Paeng, K.; Ock, C.-Y.; et al. 2023 · 2023
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