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Title: Large Scale Multi-Microscope Datasets and their Challenges Speaker: Waqas Sultani Abstract: Each year, approximately 226 million malaria cases are reported across 87 countries, with 425,600 resulting in fatalities. In 2019, 67% of these deaths were children under five. Similarly, according to GLOBOCAN 2020, leukemia is a leading cause of cancer-related deaths among individuals under 39, particularly children, accounting for 2.5% of all cancer cases with an estimated 474,519 annual incidences. Early detection through microscopic analysis of peripheral blood smears can save lives in both diseases, but this process is resource-intensive, requiring costly microscopes and skilled professionals. Additionally, many countries face a significant shortage of doctors, making it even more challenging to provide timely diagnosis. To address the subjectivity of diagnoses and the shortage of medical experts, we have developed large-scale, multi-microscope, multi-resolution Malaria and Leukemia datasets. These datasets include paired images across different microscopes and resolutions, enabling more robust model training. We have also evaluated several state-of-the-art object detectors, introduced few-shot domain adaptation techniques, and proposed partially supervised domain adaptation and detailed attribute detection methods to enhance explainability. We believe that our publicly available datasets and proposed methods will support further research and innovation in this critical area. Additional Links: Title: Few-Shot Domain Adaptive Object Detection for Microscopic Images (MICCAI-2024) Paper link: https://arxiv.org/pdf/2407.07633 Github:https://github.com/intelligentMachine... Project page: https://im.itu.edu.pk/few-shot-daodmi/ Title: A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability (MICCAI-2024) Paper link: https://arxiv.org/abs/2405.10803 Github: https://github.com/intelligentMachine... Dataset: https://drive.google.com/drive/folder... Title: Towards Low-Cost and Efficient Malaria Detection (CVPR 2022) Github: https://github.com/intelligentMachine... Speaker Bio: Waqas Sultani is an Assistant Professor at Information Technology University (ITU) and a member of the Intelligent Machines Lab. His main areas of research are Computer Vision and Deep Learning Waqas work has been published in respectable computer vision, machine learning, robotics, and remote sensing venues, such as CVPR, AAAI, ICRA, IJCV, MICCAI, ISPRS-JPRS, IEEE Trans. ITS, PAMI etc. He has been awarded the Google Research Scholar Award (2023), as a PI, for collecting a large-scale dataset for low-cost cancer detection. In 2019, he was awarded the Facebook Computer Vision for Global Challenge (CV4GC) research award for designing low-cost solutions for efficient malaria detection. He is a strong advocate of academia and industry collaborations and has recently spent one year with computer vision-based company HazenAI as a Principle Machine Learning Engineer. Before joining ITU in 2017, Waqas Sultani obtained a doctorate from the University of Central Florida under Prof Mubarak Shah, an MS from Seoul National University under Prof Jin Young Choi and BSc from U.E.T. Taxila. Currently, he is the head of the medical AI research group at ITU. ------ The MedAI Group Exchange Sessions are a platform where we can critically examine key topics in AI and medicine, generate fresh ideas and discussion around their intersection and most importantly, learn from each other. We will be having weekly sessions where invited speakers will give a talk presenting their work followed by an interactive discussion and Q&A. Our sessions are held every Monday from 1pm-2pm PST. To get notifications about upcoming sessions, please join our mailing list: https://mailman.stanford.edu/mailman/... For more details about MedAI, check out our website: https://medai.stanford.edu. You can follow us on Twitter @MedaiStanford Organized by members of the Rubin Lab (http://rubinlab.stanford.edu) and Machine Intelligence in Medicine and Imaging (MI-2) Lab: Nandita Bhaskhar (https://www.stanford.edu/~nanbhas) Amara Tariq (  / amara-tariq-475815158  ) Avisha Das (https://dasavisha.github.io/)