Fetching the paper…
Reading the bibliography…
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Inter-observer variability between general pathologists and a specialist in breast pathology in the diagnosis of lobular neoplasia, columnar cell lesions, atypical ductal hyperplasia and ductal carcinoma in situ of the breast
D.S. Gomes, S.S. Porto, D. Balabram, and H. Gobbi · 2014
Earlier work this paper cites.
Diagnostic concordance among pathologists interpreting breast biopsy specimens
J.G. Elmore, G.M. Longton, P.A. Carney, B.M. Geller, T. Onega, A.N.A. Tosteson, H.D. Nelson, MS. Pepe, K.H. Allison, S.J. Schnitt, et al · 2015
Earlier work this paper cites.
A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images
B.E. Bejnordi, G. Litjens, M. Hermsen, N. Karssemeijer, and J. van der Laak · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D.P. Kingma and J.L. Ba · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Multi-scale learning based segmentation of glands in digital colorectal pathology images
Y. Gao, W. Liu, S. Arjun, L. Zhu, V. Ratner, T. Kurc, J. Saltz, and A. Tannenbaum · 2016
Earlier work this paper cites.
Constrained deep weak supervision for histopathology image segmentation
Z. Jia, X. Huang, I. Eric, C. Chang, and Y. Xu · 2017
Earlier work this paper cites.
Weakly supervised biomedical image segmentation by reiterative learning
Q. Liang, Y. Nan, G. Coppola, K. Zou, W. Sun, D. Zhang, Y. Wang, and G. Yu · 2018
Earlier work this paper cites.
Improving whole slide segmentation through visual context - a systematic study
K. Sirinukunwattana, N.K. Alham, C. Verrill, and J. Rittscher · 2018
Earlier work this paper cites.
Attention-based deep multiple instance learning
M. Isle, J. Tomczak, and M. Welling · 2018
Earlier work this paper cites.
Graph cnn for survival analysis on whole slide pathological images
R. Li, J. Yao, X. Zhu, Y. Li, and J. Huang · 2018
Earlier work this paper cites.
Reinforced auto-zoom net: Towards accurate and fast breast cancer segmentation in whole-slide images
N. Dong, M. Kampffmeyer, X. Liang, Z. Wang, W. Dai, and E. Xing · 2018
Earlier work this paper cites.
Predicting cancer with a recurrent visual attention model for histopathology images
A. BenTaieb and G. Hamarneh · 2018
Earlier work this paper cites.
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
G. Campanella, M.G. Hanna, L. Geneslaw, A. Miraflor, V. Werneck Krauss Silva, K.J. Busam, E. Brogi, V.E. Reuter, D.S. Klimstra, and T.J. Fuchs · 2019
Cited alongside, same era.
Neural image compression for gigapixel histopathology image analysis
D. Tellez, G. Litjens, J. van der Laak, and F. Ciompi · 2019
Cited alongside, same era.
Adaptive weighting multi-field-of-view cnn for semantic segmentation in pathology
H. Tokunaga, Y. Teramoto, A. Yoshizawa, and R. Bise · 2019
Cited alongside, same era.
Learning where to see: A novel attention model for automated immunohistochemical scoring
T. Qaiser and N.M. Rajpoot · 2019
Cited alongside, same era.
Processing megapixel images with deep attention-sampling models
A. Katharopoulos and F. Fleuret · 2019
Cited alongside, same era.
Representation learning of histopathology images using graph neural networks
M. Adnan, S. Kalra, and H.R. Tizhoosh · 2020
Later among the works it cites.
Learning with differentiable pertubed optimizers
Q. Berthet, M. Blondel, O. Teboul, M. Cuturi, J.P. Vert, and F. Bach · 2020
Later among the works it cites.
Hierarchical graph representations in digital pathology
P. Pati, G. Jaume, A. Foncubierta-Rodríguez, F. Feroce, A.M. Anniciello, G. Scognamiglio, N. Brancati, M. Fiche, E. Dubruc, D. Riccio, et al · 2021
Later among the works it cites.
Accounting for dependencies in deep learning based multiple instance learning for whole slide imaging
A. Myronenko, Z. Xu, D. Yang, H.R. Roth, and D. Xu · 2021
Later among the works it cites.
Transmil: Transformer based correlated multiple instance learning for whole slide image classification
Z. Shao, H. Bian, Y. Chen, Y. Wang, J. Zhang, X. Ji, and Y. Zhang · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
Cited alongside, same era.
Lookahead optimizer: k steps forward, 1 step back
M.R. Zhang, J. Lucas, G. Hinton, and J. Ba · 2019
Cited alongside, same era.
Context-aware convolutional neural network for grading of colorectal cancer histology images
M. Shaban, R. Awan, M.M. Fraz, A. Azam, Y. Tsang, D. Snead, and N.M. Rajpoot · 2020
Cited alongside, same era.
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
J. Yao, X. Zhu, J. Jonnagaddala, N. Hawkins, and J. Huang · 2020
Cited alongside, same era.
Multi-scale domain-adversarial multiple-instance cnn for cancer subtype classification with unannotated histopathological images
N. Hashimoto, D. Fukushima, R. Koga, Y. Takagi, K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani, and I. Takeuchi · 2020
Cited alongside, same era.
Graph convolutional networks for region of interest classification in breast histopathology
B. Aygüneş, S. Aksoy, R.G. Cinbiş, K. Kösemehmetoğlu, S. Önder, and A. Üner · 2020
Cited alongside, same era.
Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution
Y. Zhao, F. Yang, Y. Fang, H. Liu, N. Zhou, J. Zhang, J. Sun, S. Yang, B. Menze, X. Fan, et al · 2020
Cited alongside, same era.
Sparseconvmil: Sparse convolutional context-aware multiple instance learning for whole slide image classification
M. Lerousseau, M. Vakalopoulou, E. Deutsch, and N. Paragios · 2021
Later among the works it cites.
Deep multi-magnification networks for multi-class breast cancer image segmentation
D.J. Ho, D.V.K. Yarlagadda, T.M. D’Alfonso, M.G. Hanna, A. Grabenstetter, P. Ntiamoah, E. Brogi, L.K. Tan, and T.J. Fuchs · 2021
Later among the works it cites.
Learning whole-slide segmentation from inexact and incomplete labels using tissue graphs
V. Anklin, P. Pati, G. Jaume, B. Bozorgtabar, A. Foncubierta-Rodriguez, J.P. Thiran, M. Sibony, M. Gabrani, and O. Goksel · 2021
Later among the works it cites.
Efficient classification of very large images with tiny objects
S. Kong and R. Henao · 2021
Later among the works it cites.
Differentiable patch selection for image recognition
J.B. Cordonnier, A. Mahendran, and A. Dosovitskiy · 2021
Later among the works it cites.
Cad systems for colorectal cancer from wsi are still not ready for clinical acceptance
S.P. Oliveira, P.C. Neto, J. Fraga, D. Montezuma, A. Monteiro, J. Monteiro, L. Ribeiro, S. Gonçalves, I.M. Pinto, and J.S. Cardoso · 2021
Later among the works it cites.
Bracs: A dataset for breast carcinoma subtyping in h&e histology images
N. Brancati, A.M. Anniciello, P. Pati, D. Riccio, G. Scognamiglio, G. Jaume, G. De Pietro, M. Di Bonito, A. Foncubierta-Rodríguez, G. Botti, et al · 2021
Later among the works it cites.
Histocartography: A toolkit for graph analytics in digital pathology
G. Jaume, P. Pati, V. Anklin, A. Foncubierta-Rodríguez, and M. Gabrani · 2021
Later among the works it cites.