Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image

Leroy, X., Zini, L., Leteurtre, E., Zerimech, F., Porchet, N., Aubert, J.P., Gosselin, B., Copin, M.C.: Morphologic subtyping of papillary renal cell carcinoma: correlation with prognosis and differential expression of muc1 between the two subtypes. Modern pathology 15

2002

Earlier work this paper cites.

Incidence and long-term prognosis of papillary compared to clear cell renal cell carcinoma – a multicentre study. European Journal of Cancer 48

2012

Earlier work this paper cites.

Delahunt, B., Cheville, J.C., Martignoni, G., Humphrey, P.A., Magi-Galluzzi, C., McKenney, J., Egevad, L., Algaba, F., Moch, H., Grignon, D.J., et al.: The international society of urological pathology (isup) grading system for renal cell carcinoma and other prognostic parameters. The American journal of surgical pathology 37

2013

Earlier work this paper cites.

Moch, H., Cubilla, A.L., Humphrey, P.A., Reuter, V.E., Ulbright, T.M.: The 2016 who classification of tumours of the urinary system and male genital organs—part a: Renal, penile, and testicular tumours. European Urology 70

2016

Earlier work this paper cites.

Cheng, J., Zhang, J., Han, Y., Wang, X., Ye, X., Meng, Y., Parwani, A., Han, Z., Feng, Q., Huang, K.: Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Research 77

2017

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017)

2017

Earlier work this paper cites.

Couture, H.D., Williams, L.A., Geradts, J., Nyante, S.J., Butler, E.N., Marron, J., Perou, C.M., Troester, M.A., Niethammer, M.: Image analysis with deep learning to predict breast cancer grade, er status, histologic subtype, and intrinsic subtype. NPJ breast cancer 4

2018

Cited alongside, same era.

Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A.L., Razavian, N., Tsirigos, A.: Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature medicine 24

2018

Cited alongside, same era.

Raza, S.E.A., Cheung, L., Shaban, M., Graham, S., Epstein, D., Pelengaris, S., Khan, M., Rajpoot, N.M.: Micro-net: A unified model for segmentation of various objects in microscopy images. Medical Image Analysis 52

2018

Cited alongside, same era.

Wong, E.C., Di Lena, R., Breau, R.H., Pouliot, F., Finelli, A., Lavallée, L.T., So, A., Tanguay, S., Fairey, A., Rendon, R., Richard, P.O., Lattouf, J.B., Kawakami, J., Mallick, R., Kapoor, A.: Morphologic subtyping as a prognostic predictor for survival in papillary renal cell carcinoma: Type 1 vs. type 2. Urologic Oncology: Seminars and Original Investigations 37

Gao, Z., Puttapirat, P., Shi, J., Li, C.: Renal cell carcinoma detection and subtyping with minimal point-based annotation in whole-slide images. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. pp. 439–448. Springer International Publishing, Cham (2020)

2020

Later among the works it cites.

Wang, S., Rong, R., Yang, D.M., Fujimoto, J., Yan, S., Cai, L., Yang, L., Luo, D., Behrens, C., Parra, E.R., Yao, B., Xu, L., Wang, T., Zhan, X., Wistuba, I.I., Minna, J., Xie, Y., Xiao, G.: Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Research 80

2020

Later among the works it cites.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale (2020)

2020

Later among the works it cites.