[38] Mingjuan L Zhang, Alan X Guo, and Christopher J VandenBussche. Morphologists overestimate the nuclear-to-cytoplasmic ratio. Cancer Cytopathology, 124(9):669-677, 2016.
[39] The University of Oklahoma Health Sciences Center. Hematopoiesis. https://www.ouhsc.edu/ histology/Text%20Sections/Hematopoiesis.html#:~:text=Promyelocyte%20(10%2D20% 20%C2%B5m)%3A,and%20primary%20(azurophilic)%20granules. Accessed: 2023-06-02.
[40] Salah Mohamed Azwai, Omran Emhammed Abdouslam, Layla Subhy Al-Bassam, Ahmed Mahdi Al Dawek, and Salah Abdul Latif Al-Izzi. Morphological characteristics of blood cells in clinically normal adult llamas (lama glama). Veterinarski Arhiv, 77(1):69-79, 2007.
[41] Catherine J Walsh and Carl A Luer. Elasmobranch hematology: identification of cell types and practical applications. The Elasmobranch Husbandry Manual: Captive Care of Sharks, Rays and Their Relatives, pages 307-323, 2004.
[42] Alyssa Tigner, Sherif A Ibrahim, and Ian Murray. Histology, white blood cell. StatPearls [Internet], 2022.
[43] Jacqueline H Carr. Introduction to peripheral blood film examination. In *Clinical Hematology Atlas*, chapter 1, pages 1–10. Elsevier Health Sciences, sixth edition, 2022.
[44] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In IEEE International Conference on Computer Vision, pages 618-626, 2017.
[45] Christian Matek, Sebastian Krappe, Christian Munzenmayer, Torsten Haferlach, and Carsten Marr. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood, The Journal of the American Society of Hematology, 138(20):1917-1927, 2021.
[46] JR Alipo-on, FI Escobar, JL Novia, MM Atienza, S Mana-ay, MJ Tan, N AlDahoul, and E Yu. Dataset for machine learning-based classification of white blood cells of the juvenile visayan warty pig. DOI: https://doi.org/10.21227/3qsb-d447., 2022.
[47] Andrea Acevedo, Anna Merino, Laura Boldú, Angel Molina, Santiago Alférez, and José Rodellar. A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes. Computers in Biology and Medicine, 134:104479, 2021.
[48] José Rodellar, Kevin Barrera, Santiago Alférez, Laura Boldú, Javier Laguna, Angel Molina, and Anna Merino. A deep learning approach for the morphological recognition of reactive lymphocytes in patients with Covid-19 infection. Bioengineering, 9(5):229, 2022.
[49] Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. Concept bottleneck models. In International Conference on Machine Learning, pages 5338-5348. PMLR, 2020.
[50] Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T Freeman, Phillip Isola, Amir Globerson, Michal Irani, et al. Explaining in style: Training a gan to explain a classifier in stylespace. In IEEE International Conference on Computer Vision, 2021.
[51] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of StyleGAN. In IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[52] Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. Ganspace: discovering interpretable gan controls. In Neural Information Processing Systems, 2020.
[53] Zongze Wu, Dani Lischinski, and Eli Shechtman. Stylespace analysis: Disentangled controls for stylegan image generation. In IEEE Conference on Computer Vision and Pattern Recognition, 2021.
[54] Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency, 2018.
[55] Mei Wang and Weihong Deng. Mitigating bias in face recognition using skewness-aware reinforcement learning. In IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[56] Roxana Daneshjou, Kailas Vodrahalli, Roberto A. Novoa, Melissa Jenkins, Weixin Liang, Veronica M Rotemberg, Justin M. Ko, Susan M. Swetter, Elizabeth E. Bailey, Olivier Gevaert, Pritam Mukherjee, Michelle Phung, Kiana Yekrang, Bradley Fong, Rachna Sahasrabudhe, Johan Allerup, Utako Okata-Karigane, James Zou, and Albert S. Chiou. Disparities in dermatology ai performance on a diverse, curated clinical image set. Science Advances, 8, 2022.
[57] Julia K Winkler, Christine Fink, Ferdinand Toberer, Alexander Enk, Teresa Deinlein, Rainer Hofmann-Wellenhof, Luc Thomas, Aimilios Lallas, Andreas Blum, Wilhelm Stolz, et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatology, 155(10):1135–1141, 2019.
[58] Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, and Christopher Ré. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In Proceedings of the ACM conference on health, inference, and learning, pages 151-159, 2020.