Md Shakhawat Hossain^1,2,#,*, Md Sahilur Rahman^2,#, Munim Ahmed², Anowar Hussen³, Zahid Ullah⁴, Mona Jamjoom⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3193-3215, 2025, DOI:10.32604/cmc.2025.065230 - 03 July 2025

Abstract One in every eight men in the US is diagnosed with prostate cancer, making it the most common cancer in men. Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients. Traditionally, urological pathologists perform the grading by scoring the morphological pattern, known as the Gleason pattern, in histopathology images. However, this manual grading is highly subjective, suffers intra- and inter-pathologist variability and lacks reproducibility. An automated grading system could be more efficient, with no subjectivity and higher accuracy and reproducibility. Automated methods presented previously… More >

Kun Yang^1,2,3, Shilong Chang¹, Yucheng Wang¹, Minghui Wang¹, Jiahui Yang¹, Shuang Liu^1,2,3, Kun Liu^1,2,3, Linyan Xue^1,2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 393-410, 2024, DOI:10.32604/cmc.2023.044994 - 30 January 2024

Abstract Clear cell renal cell carcinoma (ccRCC) represents the most frequent form of renal cell carcinoma (RCC), and accurate International Society of Urological Pathology (ISUP) grading is crucial for prognosis and treatment selection. This study presents a new deep network called Multi-scale Fusion Network (MsfNet), which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images. The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity. The model was trained and tested using 90 Hematoxylin and Eosin (H&E) stained whole slide images (WSIs), which… More >

GIUSEPPINA BONIZZI, LORENZO ZATTONI, NICOLA FUSCO^*

Oncology Research, Vol.29, No.4, pp. 229-233, 2021, DOI:10.32604/or.2022.024892 - 31 August 2022

Abstract Digital Pathology is becoming more and more important to achieve the goal of precision medicine. Advances in whole-slide imaging, software integration, and the accessibility of storage solutions have changed the pathologists’ clinical practice, not only in terms of laboratory workflow but also for diagnosis and biomarkers analysis. In parallel with the pathology setting advancement, translational medicine is approaching the unprecedented opportunities unrevealed by artificial intelligence (AI). Indeed, the increased usage of biobanks’ datasets in research provided new challenges for AI applications, such as advanced algorithms, and computer-aided techniques. In this scenario, machine learning-based approaches are More >

Afzan Adam^1,*, Abdul Hadi Abd Rahman¹, Nor Samsiah Sani¹, Zaid Abdi Alkareem Alyessari¹, Nur Jumaadzan Zaleha Mamat², Basela Hasan³

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 761-777, 2021, DOI:10.32604/cmc.2021.014599 - 12 January 2021

Abstract Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being… More >