DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system
Tài liệu tham khảo
Al-Janabi, 2012, Digital pathology: current status and future perspectives, Histopathology, 61, 1, 10.1111/j.1365-2559.2011.03814.x
Aresta, 2019, Bach: grand challenge on breast cancer histology images, Med. Image Anal., 56, 122, 10.1016/j.media.2019.05.010
Bandi, 2018, From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge, IEEE Trans. Med. Imaging, 38, 550, 10.1109/TMI.2018.2867350
Bejnordi, 2017, Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, JAMA, 318, 2199, 10.1001/jama.2017.14585
Belli, 2014, Outcomes of surgical treatment of primary signet ring cell carcinoma of the colon and rectum: 22 cases reviewed with literature, Int. Surg., 99, 691, 10.9738/INTSURG-D-14-00067.1
Borovec, 2018, Benchmarking of image registration methods for differently stained histological slides, 3368
Bray, 2018, Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA, 68, 394
Byun, 2006, Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images, Mol. Vis., 12, 949
Caicedo, 2019, Nucleus segmentation across imaging experiments: the 2018 data science bowl, Nat. Methods, 16, 1247, 10.1038/s41592-019-0612-7
Campanella, 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat. Med., 25, 1301, 10.1038/s41591-019-0508-1
Chen, 2021, An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning, Nat. Commun., 12, 1
Chen, 2016, Mitosis detection in breast cancer histology images via deep cascaded networks
Ciompi, F., Veta, M., Albarqouni, S., Jiao, Y., Tan, T., Zhang, L., Jeroen van der, L., Nasir, R., 2019. Lymphocyte assessment hackathon. https://lysto.grand-challenge.org/.
Conde-Sousa, E., Vale, J., Feng, M., Xu, K., Wang, Y., Della Mea, V., La Barbera, D., Montahaei, E., Baghshah, M. S., Turzynski, A., et al., 2021. Herohe challenge: assessing her2 status in breast cancer without immunohistochemistry or in situ hybridization. arXiv preprint arXiv:2111.04738
Da, 2022, Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning, Sci. Rep., 12, 1, 10.1038/s41598-021-03984-4
Dalal, 2005, Histograms of oriented gradients for human detection, vol. 1, 886
Falk, 2019, U-Net: deep learning for cell counting, detection, and morphometry, Nat. Methods, 16, 67, 10.1038/s41592-018-0261-2
Girshick, 2015, Fast R-CNN, 1440
Girshick, 2014, Rich feature hierarchies for accurate object detection and semantic segmentation, 580
Grabovac, 2020, Gastrointestinal Cancer, 128
Gupta, 2017, Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computer assisted automated disease diagnostic tool in multiple myeloma, Clin. Lymphoma, Myeloma Leuk., 17, e99
Hamilton, 2000
He, 2017, Mask R-CNN, 2961
He, 2016, Deep residual learning for image recognition, 770
Hou, 2019, Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images, Pattern Recognit., 86, 188, 10.1016/j.patcog.2018.09.007
Huang, 2017, Densely connected convolutional networks, 4700
Karimi, 2019, Deep learning-based Gleason grading of prostate cancer from histopathology imagesrole of multiscale decision aggregation and data augmentation, IEEE J. Biomed. Health Inform., 24, 1413, 10.1109/JBHI.2019.2944643
Kepil, 2019, Immunohistochemical and genetic features of mucinous and signet-ring cell carcinomas of the stomach, colon and rectum: a comparative study, Int. J. Clin. Exp. Pathol., 12, 3483
Kim, 2021, Paip 2019: liver cancer segmentation challenge, Med. Image Anal., 67, 101854, 10.1016/j.media.2020.101854
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097
Kumar, 2019, A multi-organ nucleus segmentation challenge, IEEE Trans. Med. Imaging, 39, 1380, 10.1109/TMI.2019.2947628
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Lee, 2003, An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition, IEEE Trans. Neural Netw., 14, 680, 10.1109/72.846739
Li, 2021, Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning, 14318
Li, 2021, Hybrid supervision learning for pathology whole slide image classification, 309
Li, 2019, Accurate nuclear segmentation with center vector encoding, 394
Li, 2019, Dsfd: dual shot face detector, 5060
Li, 2019, Signet ring cell detection with a semi-supervised learning framework, 842
Li, Z., Hu, Z., Xu, J., Tan, T., Chen, H., Duan, Z., Liu, P., Tang, J., Cai, G., Ouyang, Q., et al., 2018a. Computer-aided diagnosis of lung carcinoma using deep learning-a pilot study. arXiv preprint arXiv:1803.05471
Li, 2018, Large-scale retrieval for medical image analytics: acomprehensive review, Med. Image Anal., 43, 66, 10.1016/j.media.2017.09.007
Lin, 2017, Focal loss for dense object detection, 2980
Litjens, 2017, A survey on deep learning in medical image analysis, Med. Image Anal., 42, 60, 10.1016/j.media.2017.07.005
Long, 2015, Fully convolutional networks for semantic segmentation, 3431
Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis., 60, 91, 10.1023/B:VISI.0000029664.99615.94
Lu, 2021, Data-efficient and weakly supervised computational pathology on whole-slide images, Nat. Biomed. Eng., 5, 555, 10.1038/s41551-020-00682-w
Lu, 2013, Automated nucleus and cytoplasm segmentation of overlapping cervical cells, 452
Mármol, 2017, Colorectal carcinoma: a general overview and future perspectives in colorectal cancer, Int. J. Mol. Sci., 18, 197, 10.3390/ijms18010197
Maurer, 2003, A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions, IEEE Trans. Pattern Anal. Mach. Intell., 25, 265, 10.1109/TPAMI.2003.1177156
Messerini, 1995, Primary signet-ring cell carcinoma of the colon and rectum, Diseases Colon Rectum, 38, 1189, 10.1007/BF02048335
Muhammad, 2019, Unsupervised subtyping of cholangiocarcinoma using a deep clustering convolutional autoencoder, 604
Naylor, 2018, Segmentation of nuclei in histopathology images by deep regression of the distance map, IEEE Trans. Med. Imaging, 38, 448, 10.1109/TMI.2018.2865709
Nir, 2018, Automatic grading of prostate cancer in digitized histopathology images: learning from multiple experts, Med. Image Anal., 50, 167, 10.1016/j.media.2018.09.005
Peikari, 2017, Automatic cellularity assessment from post-treated breast surgical specimens, Cytom. Part A, 91, 1078, 10.1002/cyto.a.23244
Pernot, 2015, Signet-ring cell carcinoma of the stomach: impact on prognosis and specific therapeutic challenge, World J. Gastroenterol., 21, 11428, 10.3748/wjg.v21.i40.11428
Qu, 2020, Weakly supervised deep nuclei segmentation using partial points annotation in histopathology images, IEEE Trans. Med. Imaging, 39, 3655, 10.1109/TMI.2020.3002244
Redmon, J., Farhadi, A., 2018. Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767
Ren, 2015, Faster R-CNN: towards real-time object detection with region proposal networks, 91
Ronneberger, 2015, U-Net: convolutional networks for biomedical image segmentation, 234
Schmauch, 2020, A deep learning model to predict RNA-Seq expression of tumours from whole slide images, Nat. Commun., 11, 1, 10.1038/s41467-020-17678-4
Shao, 2021, Transmil: transformer based correlated multiple instance learning for whole slide image classification, Adv. Neural Inf. Process. Syst., 34, 2136
Shen, 2017, Deep learning in medical image analysis, Annu. Rev. Biomed. Eng., 19, 221, 10.1146/annurev-bioeng-071516-044442
Shi, 2016, Histopathological image classification with color pattern random binary hashing-based pcanet and matrix-form classifier, IEEE J. Biomed. Health Inform., 21, 1327, 10.1109/JBHI.2016.2602823
Shi, 2020, Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis, Med. Image Anal., 60, 101624, 10.1016/j.media.2019.101624
Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Sirinukunwattana, 2017, Gland segmentation in colon histology images: the glas challenge contest, Med. Image Anal., 35, 489, 10.1016/j.media.2016.08.008
Soille, 2013
Swiderska-Chadaj, 2019, Learning to detect lymphocytes in immunohistochemistry with deep learning, Med. Image Anal., 58, 101547, 10.1016/j.media.2019.101547
Szegedy, 2015, Going deeper with convolutions, 1
Szegedy, 2016, Rethinking the inception architecture for computer vision, 2818
Tan, 2019, Efficientnet: Rethinking model scaling for convolutional neural networks, 6105
Treanor, 2007, Pathology of colorectal cancer, Clin. Oncol., 19, 769, 10.1016/j.clon.2007.08.012
Veeling, 2018, Rotation equivariant CNNs for digital pathology, 210
Veta, 2019, Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge, Med. Image Anal., 54, 111, 10.1016/j.media.2019.02.012
Veta, 2011, Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images, 618
Wang, 2017, Histopathological image classification with bilinear convolutional neural networks, 4050
Wen, 2016, A discriminative feature learning approach for deep face recognition, 499
Xie, 2017, Aggregated residual transformations for deep neural networks, 1492
Xing, 2016, Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review, IEEE Rev. Biomed. Eng., 9, 234, 10.1109/RBME.2016.2515127
Yang, 2016, 3D segmentation of glial cells using fully convolutional networks and k-terminal cut, 658
Yin, 2019, Burden of colorectal cancer in China, 1990–2017: findings from the global burden of disease study 2017, Chin. J. Cancer Res., 31, 489, 10.21147/j.issn.1000-9604.2019.03.11
Yu, 2021, Large-scale gastric cancer screening and localization using multi-task deep neural network, Neurocomputing, 448, 290, 10.1016/j.neucom.2021.03.006
Zhang, 2014, Towards large-scale histopathological image analysis: hashing-based image retrieval, IEEE Trans. Med. Imaging, 34, 496, 10.1109/TMI.2014.2361481
Zhang, 2015, Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval, 5361
Zhang, 2015, High-throughput histopathological image analysis via robust cell segmentation and hashing, Med. Image Anal., 26, 306, 10.1016/j.media.2015.10.005
Zhang, 2014, Mining histopathological images via composite hashing and online learning, 479
Zheng, G., Li, S., Belavy, D., 2020. https://ivdm3seg.weebly.com/.