Yang J (2014) Treatment of helicobacter pylori infection: current status and future concepts. World J Gastroenterol 20(18):5283–93
Article CAS PubMed Central PubMed Google Scholar
Batts KP, Ketover S, Kakar S, Krasinskas AM, Mitchell KA, Wilcox R, Westerhoff M, Rank J, Gibson J, Mattia AR, Cummings OW, Davison JM, Naini JMBV, Dry SM, Yantiss RK (2013) Gastrointestinal pathology society. appropriate use of special stains for identifying helicobacter pylori: recommendations from the rodger c. haggitt gastrointestinal pathology society. Am J Surg Pathol 37(11):12–22
Salto-Tellez M, Maxwell P, Hamilton P (2019) Artificial intelligence-the third revolution in pathology. Histopathology 74(3):372–376
Klein S, Gildenblat J, Ihle MA (2020) al: deep learning for sensitive detection of helicobacter pylori in gastric biopsies. BMC gastroenterology 20(1):1–11
Liscia DS, D’Andrea M, Biletta E, Bellis D, Demo K, Ferrero F, Petti A, Butinar R, D’Andrea E, Davini G (2022) Use of digital pathology and artificial intelligence for the diagnosis of helicobacter pylori in gastric biopsies. Pathologica 114(4):295
Article PubMed Central PubMed Google Scholar
Ibrahim AU, Dirilenoğlu F, Hacisalihoğlu UP, Ilhan A, Mirzaei O (2024) Classification of h. pylori infection from histopathological images using deep learning. J Imaging Inf Med 37:1–10
Zhou S, Marklund H, Blaha O, Desai M, Martin B, Bingham D, Berry GJ, Gomulia E, Ng AY, Shen J (2020) Deep learning assistance for the histopathologic diagnosis of helicobacter pylori. Intell -Based Med 1:100004
Lin Y-J, Chen C-C, Lee C-H, Yeh C-Y, Jeng Y-M (2023) Two-tiered deep-learning-based model for histologic diagnosis of helicobacter gastritis. Histopathology 83(5):771–781
Huang S-C, Chen C-C, Lan J, Hsieh T-Y, Chuang H-C, Chien M-Y, Ou T-S, Chen K-H, Wu R-C, Liu Y-J et al (2022) Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings. Nature Commun 13(1):3347
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929
Le-Khac PH, Healy G, Smeaton AF (2020) Contrastive representation learning: a framework and review. IEEE Access 8:193907–193934. https://doi.org/10.1109/ACCESS.2020.3031549
Feng Y, Zhang L, Mo J (2020) Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Tran Computational Biol Bioinf 17(1):91–101. https://doi.org/10.1109/TCBB.2018.2858763
Yong BX, Brintrup A (2022) Do autoencoders need a bottleneck for anomaly detection? IEEE Access 10:78455–78471
Chen RJ, Ding T, Lu MY, Williamson DF, Jaume G, Song AH, Chen B, Zhang A, Shao D, Shaban M et al (2024) Towards a general-purpose foundation model for computational pathology. Nature Med 30(3):850–862
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36
Obuchowski NA, McCLISH DK (1997) Sample size determination for diagnostic accuracy studies involving binormal roc curve indices. Statistics Med 16(13):1529–1542
Hahne C, Aggoun A (2021) Plenopticam v1.0: a light-field imaging framework. IEEE Tran Image Process 30:6757–6771
Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: III HD, Singh A (eds) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp 1597–1607. PMLR, ???
Wilm F, Fragoso M, Bertram CA, Stathonikos N, Öttl M, Qiu J, Klopfleisch R, Maier AK, Aubreville M, Breininger K (2022) Mind the gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology. ArXiv abs/2211.16141
留言 (0)