[1] P. Ferraro, A. Wax, Z. Zalevsky. Coherent Light Microscopy: Imaging and Quantitative Phase Analysis, 46(2011).

[2] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nature photonics, 12, 578-589(2018).

[3] S. Hasegawa, Y. Hayasaki, N. Nishida. Holographic femtosecond laser processing with multiplexed phase Fresnel lenses. Opt. Lett., 31, 1705-1707(2006).

[4] T. Kreis. Handbook of Holographic Interferometry: Optical and Digital Methods(2006).

[5] V. Astratov, C. Hu, G. Popescu. Quantitative phase imaging: principles and applications. Label-Free Super-Resolution Microscopy, 1-24(2019).

[6] L. Tian, L. Waller. 3D intensity and phase imaging from light field measurements in an led array microscope. Optica, 2, 104-111(2015).

[7] D. J. Brady et al. Compressive holography. Opt. Express, 17, 13040-13049(2009).

[8] N. Antipa et al. DiffuserCAM: lensless single-exposure 3D imaging. Optica, 5, 1-9(2017).

[9] T. Shimobaba et al. In-line digital holographic microscopy using a consumer scanner. Sci. Rep., 3, 2664(2013).

[10] A. Ozcan, E. McLeod. Lensless imaging and sensing. Annu. Rev. Biomed. Eng., 18, 77-102(2016).

[11] G. Zheng, R. Horstmeyer, C. Yang. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics, 7, 739-745(2013).

[12] H. N. Chapman, K. A. Nugent. Coherent lensless X-ray imaging. Nat. Photonics, 4, 833-839(2010).

[13] R. W. Gerchberg. A practical algorithm for the determination of plane from image and diffraction pictures. Optik, 35, 237-246(1972).

[14] Z. Yuan, H. Wang. Phase retrieval via reweighted Wirtinger flow. Appl. Opt., 56, 2418-2427(2017).

[15] Z. Yuan, H. Wang, Q. Wang. Phase retrieval via sparse Wirtinger flow. J. Comput. Appl. Math., 355, 162-173(2019).

[16] E. J. Candes, X. Li, M. Soltanolkotabi. Phase retrieval via Wirtinger flow: theory and algorithms. IEEE Trans. Inf. Theory, 61, 1985-2007(2015).

[17] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758-2769(1982).

[18] M. L. Cruz, A. Castro, V. Arrizón. Phase retrieval in digital holographic microscopy using a Gerchberg-Saxton algorithm. Proc. SPIE, 7072, 70721C(2008).

[19] T. Latychevskaia. Iterative phase retrieval for digital holography: tutorial. J. Opt. Soc. Amer. A, 36, D31-D40(2019).

[20] C. Lee et al. Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat. Mach. Intell., 5, 35-45(2023).

[21] Y. Rivenson, Y. Wu, A. Ozcan. Deep learning in holography and coherent imaging. Light: Sci. Appl., 8, 85(2019).

[22] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light: Sci. Appl., 7, 17141-17141(2018).

[23] L. Huang et al. Self-supervised learning of hologram reconstruction using physics consistency. Nat. Mach. Intell., 5, 895-907(2023).

[24] G. Situ. Deep holography. Light: Adv. Manuf., 3, 278-300(2022).

[25] Y. Wu et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 5, 704-710(2018).

[26] K. Yan et al. Fringe pattern denoising based on deep learning. Opt. Commun., 437, 148-152(2019).

[27] D. Yin et al. Digital holographic reconstruction based on deep learning framework with unpaired data. IEEE Photonics J., 12, 1-12(2019).

[28] Y. Zhang et al. PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets. Opt. Express, 29, 19593-19604(2021).

[29] Z. Hong et al. Out-of-distribution detection in medical image analysis: a survey(2024).

[30] D. Zimmerer et al. Mood 2020: a public benchmark for out-of-distribution detection and localization on medical images. IEEE Trans. Med. Imaging, 41, 2728-2738(2022).

[31] A. Choudhary et al. Advancing medical imaging informatics by deep learning-based domain adaptation. Yearbook Med. Inf., 29, 129-138(2020).

[32] A. Gholami et al. A novel domain adaptation framework for medical image segmentation. Lect. Notes Comput. Sci., 11384, 289-298(2019).

[33] A. Sanner, C. Gonzalez, A. Mukhopadhyay. How reliable are out-of-distribution generalization methods for medical image segmentation?. Lect. Notes Comput. Sci., 13024, 604-617(2021).

[34] S. Kumari, P. Singh. Deep learning for unsupervised domain adaptation in medical imaging: recent advancements and future perspectives. Comput. Biol. Med., 170, 107912(2023).

[35] Y. Zhang et al. Collaborative unsupervised domain adaptation for medical image diagnosis. IEEE Trans. Image Process., 29, 7834-7844(2020).

[36] H. Guan, M. Liu. Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng., 69, 1173-1185(2021).

[37] O. Zhang, J.-B. Delbrouck, D. L. Rubin. Out of distribution detection for medical images. Lect. Notes Comput. Sci., 12959, 102-111(2021).

[38] C. Villani. Optimal Transport: Old and New, 338(2009).

[39] B. Sim et al. Optimal transport driven cycleGAN for unsupervised learning in inverse problems. SIAM J. Imaging Sci., 13, 2281-2306(2020).

[40] J. Li et al. A comprehensive survey on source-free domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell., 46, 5743-5762(2024).

[41] M. Toldo et al. Unsupervised domain adaptation in semantic segmentation: a review. Technologies, 8, 35(2020).

[42] A. Sinha et al. Lensless computational imaging through deep learning. Optica, 4, 1117-1125(2017).

[43] L. Huang et al. Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks. ACS Photonics, 8, 1763-1774(2021).

[44] M. Rogalski et al. Physics-driven universal twin-image removal network for digital in-line holographic microscopy. Opt. Express, 32, 742-761(2023).

[45] Y. Wu et al. Distributed contrastive learning for medical image segmentation. Med. Image Anal., 81, 102564(2022).

[46] K. Chaitanya et al. Contrastive learning of global and local features for medical image segmentation with limited annotations, 12546-12558(2020).

[47] X. Wang et al. Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal., 81, 102559(2022).

[48] S. A. Rizvi et al. Local contrastive learning for medical image recognition, 1236(2024).

[49] Y. Lu et al. Contrastive learning meets transfer learning: a case study in medical image analysis. Proc. SPIE, 12033, 120332Q(2022).

[50] X. Zhang et al. Pyramid pixel context adaption network for medical image classification with supervised contrastive learning. IEEE Trans. Neural Netw. Learn. Syst.(2024).

[51] Z. Zhu et al. MURCL: multi-instance reinforcement contrastive learning for whole slide image classification. IEEE Trans. Med. Imaging, 42, 1337-1348(2022).

[52] J. W. Goodman. Introduction to Fourier Optics(2005).

[53] T. Latychevskaia, H.-W. Fink. Practical algorithms for simulation and reconstruction of digital in-line holograms. Appl. Opt., 54, 2424-2434(2015).

[54] T. Chen et al. A simple framework for contrastive learning of visual representations, 1597-1607(2020).

[55] T. O’Connor et al. Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy. Biomed. Opt. Express, 11, 4491-4508(2020).

[56] Y. Zhang et al. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Light: Sci. Appl., 9, 78(2020).

[57] A. Butola et al. High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition. Sci. Rep., 10, 13118(2020).

[58] Y. Jo et al. Holographic deep learning for rapid optical screening of anthrax spores. Sci. Adv., 3, e1700606(2017).

[59] Y. Rivenson et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng., 3, 466-477(2019).

[60] N. Pillar, A. Ozcan. Virtual tissue staining in pathology using machine learning. Expert Rev. Mol. Diagn., 22, 987-989(2022).

[61] M. Fleming et al. Colorectal carcinoma: pathologic aspects. J. Gastrointest. Oncol., 3, 153(2012).

[62] R. Caruso et al. Histologic coagulative tumour necrosis as a prognostic indicator of aggressiveness in renal, lung, thyroid and colorectal carcinomas: a brief review. Oncol. Lett., 3, 16-18(2012).

[63] R. S. Gonzalez et al. Micropapillary colorectal carcinoma: clinical, pathological and molecular properties, including evidence of epithelial–mesenchymal transition. Histopathology, 70, 223-231(2017).

[64] M. J. Pollheimer et al. Tumor necrosis is a new promising prognostic factor in colorectal cancer. Hum. Pathol., 41, 1749-1757(2010).

[65] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015).

[66] K. He et al. Deep residual learning for image recognition, 770-778(2016).

[67] V. Dumoulin, J. Shlens, M. Kudlur. A learned representation for artistic style(2016).

[68] J. Hu, L. Shen, G. Sun. Squeeze-and-excitation networks, 7132-7141(2018).

[69] Z. Ren, Z. Xu, E. Y. Lam. Learning-based nonparametric autofocusing for digital holography. Optica, 5, 337-344(2018).

[70] X. Glorot, Y. Bengio. Understanding the difficulty of training deep feedforward neural networks, 249-256(2010).

[71] D. P. Kingma. Adam: a method for stochastic optimization(2014).