Dr. Mahdi S. Hosseini 

Bio: Mahdi S. Hosseini is an Assistant Professor in Computer Science and Software Engineering (CSSE) at Concordia University, Adjunct faculty in Pathology at McGill University, and an Affiliate Member of Mila–Quebec AI Institute. He directs the Atlas Analytics Lab (CSSE@Concordia) and holds the Gina Cody Research Chair (GCRC) – Research and Innovation. He is a Lead Area Chair for CVPR 2025-26, Broadening Participation Chair of ICCV 2025, and served as AC for CVPR 2022–24, NeurIPS 2023–25, and ECCV 2024. He received the Amazon Research Award (Generative AI, Fall 2023) for work on auto-populating synoptic histopathology reports. His research at Atlas Analytics advances foundational deep learning and computer vision for efficient computational pathology in industry and clinical settings. He has extensive experience in working with digital pathology industry in different sectors including quality assessment and enhancements of image acquisition systems of Whole Slide Image (WSI) Scanners, development of deep learning solutions in computational pathology by contributing dataset creation and software developments in cancer diagnostics. He currently supervises several HQP trainees and has graduated six master’s students since August 2022. His goal, in partnership with hospitals, health-tech, and industry, is to develop Computer-Aided Diagnosis (CAD) systems integrated into clinical pathology for cancer diagnosis, prognosis, and treatment. 

Title: Foundation Models in Computational Pathology: Clinical Diagnostics and Beyond

Abstract:

The computational advantages of deep learning in AI, integrated with giga-pixel whole slide image (WSI) in  pathology, has led to the emergence of a new field called Computational Pathology (CPath) that is poised to transform clinical pathology globally. CPath is dedicated to the creation of automated tools that address and aid steps in the clinical workflow for diagnosing and treatment of cancer diseases. With increasing advancements of foundation models in deep learning, the research focus in this field has expanded to broad range of domains. In this seminar talk I will cover two topics: first, I will present the ongoing research trends in deep learning and computational pathology for developing foundational models. We investigate this from “data preprocessing”, “self-supervised pretraining” and “downstream tasking” for clinical applications. Second, I will delve into the ongoing research that we are currently addressing from Atlas Analytics Lab@Concordia e.g. efficient slide processing algorithms, deep learning optimization, encoder designs, prompt-engineering in vision-language models, and understanding concept alignments in foundation models. I will conclude this talk with the objectives of discussing challenges in clinics and research for future AI developments in CPath and how they can facilitate the transformational changes in clinical pathology.

دکتر هشام فیلی (صفحه شخصی)

دکتر بابک نجار اعرابی (صفحه شخصی)

عنوان: نگاهی به هوش مصنوعی در افق آینده نزدیک

چکیده: هوش مصنوعی به عنوان یک تکنولوژی عام منظوره و ساختارشکن در حال تغییر دادن چهره زندگی ماست. این روند به کجا می‌رود؟ اهالی علم و تکنولوژی باید چه آینده‌ای را در افق نزدیک پیش رو در نظر داشته باشند؟ در این سخنرانی به بررسی گزارش‌ها و تحلیل‌ها و شاخص‌های بین‌المللی و داخلی در ارزیابی هوش مصنوعی و آینده نگاری آن خواهیم پرداخت.

  دکتر بهروز مینایی (صفحه شخصی)