Adeniyi Adedeji Ahmed, Adeshina Adetunji Steve, Muhammad Aliyu Suleiman, Aleshinloye Yusuf Abass
Abstract
The utilization of computer-aided diagnosis (CAD) systems based on deep learning technologies for histopathology images analysis and cancer detection has been highly successful. However, many of these techniques make use of regular Red-Green-Blue (RGB) whole slide images, which offers only limited spectral data for precise tissue characterization, making it difficult for the model to recognize pathological features in biological tissues. This ultimately pushes RGB-based histopathology systems gradually towards an information bottleneck, given the increasing demand for higher diagnostic precision and early disease detection. In this regard, Hyperspectral imaging (HSI) can be considered as an alternative imaging method, allowing comprehensive spectral-spatial information obtained from multiple wavelength bands, thereby improving tissue analysis capabilities and disease detection. Recently, HSI have earned increased attention in computational pathology due to its capability to increase diagnostic accuracy compared to the conventional RGB imaging technology. This paper presents a review of Hyperspectral imaging in computer- aided histopathology diagnosis. The study examines the basics of hyperspectral imaging HSI, deep learning approaches in HSI, publicly available data sets, advantages of HSI in comparison to conventional RGB- based systems, and current challenges in HSI development along with possible avenues of further investigation. The review reveals that hyperspectral imaging possesses significant potential for advancing next-level digital pathology and precision diagnostic applications.
Keywords
Hyperspectral Imaging, Histopathology, Computer-Aided Diagnosis, Deep Learning, Spectral- Spatial Learning, Whole Slide Imaging, Medical Imaging, Artificial intelligence.
Conclusion
Hyperspectral imaging technique is becoming more popular in the field of computer-aided histopathology diagnosis, owing to the capability of the technique to provide both spectral and spatial information regarding the tissue [25]. In contrast to conventional imaging systems which focus mainly on the structural details of the tissues, HSI is able to generate biochemical and spectral features that improve the process of tissue classification and discrimination [26], [34]. AI techniques such as deep learning, spectral-spatial convolutional neural networks, transformers, and self-supervised learning have recently revolutionized HSI-based computer-aided diagnostic systems [36], [33], [37]. However, despite all these improvements, there are still many obstacles that prevent the implementation of such HSI systems in a clinic, which include issues of high-dimensionality, inadequate benchmark datasets, difficulties with data annotation, computational power demands, non-standard acquisition protocols, and integration into clinical workflow [45], [46]. Despite these problems, research is still actively being conducted into such fields as explainable AI, federated learning, multimodal imaging, lightweight transformer models, and foundation models for hyperspectral pathology [6], [59] [49]. It is highly probable that future computational pathology systems will combine hyperspectral imaging with multimodal AI models that can utilize spectral, morphological, genomic, and clinical data in order to achieve better precision medicine [63]. Therefore, hyperspectral imaging shows great potential for revolutionizing the field of digital pathology and next-generation AI-based cancer diagnostics.
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How to Cite This Paper
Adeniyi Adedeji Ahmed, Adeshina Adetunji Steve, Muhammad Aliyu Suleiman, Aleshinloye Yusuf Abass (2026). Hyperspectral Imaging for Computer-Aided Histopathology Diagnosis: Opportunities, Challenges, and Future Directions.. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
