Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection
Abstract
Abstract This study compares various preprocessing techniques for hyperspectral deep learning–based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as well varying levels of blood and light reflections removal. We also examine how the size of the patches extracted from the hyperspectral data affects the models’ performance. We additionally explore various strategies to mitigate our dataset’s imbalance (where cancerous tissues are underrepresented). Our results indicate that. Scaling: Standardization significantly improves both sensitivity and specificity compared to Normalization. Larger input patch sizes enhance performance by capturing more spatial context. Noise reduction unexpectedly degrades performance. Blood filtering is more effective than filtering reflected light pixels, although neither approach produces significant results. By carefully maintaining consistent testing conditions, we ensure a fair comparison across preprocessing methods and reproducibility. Our findings highlight the necessity of careful preprocessing selection to maximize deep learning performance in medical imaging applications.
Article Details
Authors (8)
Mariia Tkachenko
Benjamin Huber
Serhii Hamotskyi
Boris Jansen-Winkeln
Ines Gockel
From Bielefeld University, Medical School and University Medical Center Ostwestfalen-Lippe, Campus Hospital Lippe, Detmold, Germany (J.H.); the Department of Radiation Oncology, Medical University of Graz, Graz, Austria (T.B.); the Clinical Trials Unit, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany (C.S.); the Institute of Surgical Pathology, University Medical Center Freiburg, Germany (P.B.); the Department of Surgery, University Medical Center Schleswig-Holstein–Campus Lübeck, Lübeck, Germany (B.K., T.K.); Comprehensive Cancer Center Augsburg, Faculty of Medicine, University of Augsburg, Augsburg, Germany (R.C.); the Department of General and Visceral Surgery, University Medical Center Freiburg, Freiburg, Germany (S.U.); the Department of General, Visceral, and Thoracic Surgery, University Medical Center Hamburg–Eppendorf, Hamburg, Germany (J.R.I.); the Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute and San Raffaele Vita-Salute Universi...
Thomas Neumuth
Hannes Köhler
Marianne Maktabi