PhD Defense: Nikita Shvetsov

Nikita Shvetsov will defend his PhD thesis for the PhD degree in Science at UiT The Arctic University of Norway, Wednesday August 27th, 12:15 CET. The title of his thesis is "Optimizing pathology workflows: A practical deep learning approach for cell-level biomarker quantification".

The trial lecture title is "Quantum computing, a roadmap to the impact on AI"

The trial lecture and defense will be streamed via the following links:

Trial lecture (10:00 - 11:00 CET)

PhD defense (12:15 - 15:00 CET)

Summary of the thesis

Computational pathology continues to advance through deep learning. However, interactive analysis of gigapixel whole slide images remains challenging due to high computational demands and long processing times. Variability in tissue morphology and staining quality further complicates automated cell detection and quantification in clinical workflows. In this study, we present an integrated and efficient framework for simultaneous cell segmentation and classification, demonstrating its utility through the quantification of tumor-infiltrating lymphocytes (TILs) in non‐small cell lung cancer (NSCLC). Initially, we adapt a state‐of‐the‐art deep learning model as a baseline method for TIL quantification, achieving high segmentation and classification performance (Dice score: 0.84, F1‐score: 0.75). The results correlate with standard immunohistochemical CD8 staining and patient survival outcomes. Building on this baseline, we develop an automated pipeline (“Fast TILs”) that integrates patch extraction, the selection of prognostically significant regions, and rapid, simultaneous cell segmentation and classification. This optimized pipeline reduces processing time by approximately 95%, performing analysis in approximately four minutes per slide while achieving superior prognostic accuracy compared to conventional CD8 IHC (concordance index: 0.649 vs. 0.599). Moreover, it generates visual overlays and quantitative metrics, eliminating human sampling bias and remaining adaptable to other biomarkers. To further improve clinical usability, we refine our methodology into a lightweight model. Our multi-step development strategy involves dataset enhancement via cross‐relabeling, knowledge distillation from larger foundation models, and integration with open digital pathology platforms such as QuPath. This lightweight model demonstrates robust segmentation and classification performance (coefficient of determination: 0.749, panoptic quality score: 0.496), distinguishing between benign, malignant, and inflammatory cell populations interactively in resource‐limited clinical settings. Overall, this work provides a practical, accurate, and computationally efficient approach for integrating deep learning–based cell quantification into clinical pathology workflows. Further validation in larger patient cohorts is necessary to confirm clinical robustness and generalizability.

Supervisory Committee:

• Professor Lars Ailo Bongo, Institutt for informatikk, UiT (Main Supervisor)
• Associate Professor Thomas Karsten Kilvær, Institutt for klinisk medisin, UiT
• Professor Lill-Tove Rasmussen Busund, Institutt for medisinsk biologi, UiT
• Associate Professor Kajsa Møllersen, Institutt for samfunnsmedisin, UiT

Evaluation Committee:

• 1st Opponent: Associate professor Lee A.D. Cooper, Director, Center for Computational Imaging and Signal Analytics, Chicago, USA Northwestern University Feinberg School of Medicine
• 2nd Opponent: Professor Tone F. Bathen, Institutt for sirkulasjon og bildediagnostikk Fakultet for medisin og helsevitenskap, NTNU
• Internal member and leader of the committee: Associate professor Loïc Guégan, IFI, UiT