Design of a hybrid deep learning system for discriminating between low- and high-grade colorectal cancer lesions, using microscopy images of IHC stained for AIB1 expression biopsy material

• Published in: Machine vision and applications 2021-05, Vol.32 (3), Article 58
• Main Authors: Theodosi, Angeliki, Ouzounis, Sotiris, Kostopoulos, Spiros, Glotsos, Dimitris, Kalatzis, Ioannis, Tzelepi, Vassiliki, Ravazoula, Panagiota, Asvestas, Pantelis, Cavouras, Dionisis, Sakellaropoulos, George
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Biopsy; Cancer; Classifiers; Colorectal cancer; Communications Engineering; Computer Science; Convolution; Deep learning; Design; Discrimination; Hybrid systems; Image contrast; Image Processing and Computer Vision; Lenses; Lesions; Machine learning; Medical imaging; Multiprocessing; Networks; Neural networks; Original Paper; Pattern Recognition; Performance evaluation; Support vector machines; Systems design; Texture; Vision systems
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-021-01184-8

To design a hybrid deep learning system (hDL-system) for discriminating low-grade from high-grade colorectal cancer (CRC) lesions, using immunohistochemically stained biopsy specimens for AIB1 expression. AIB1 has oncogenic function in tumour genesis, and it is an important prognostic factor regarding various types of cancers, including CRC. Clinical material consisted of biopsy specimens of sixty-seven patients with verified CRC (26 low-grade, 41 high-grade cases). From each patient, we digitized images, at × 50 and × 200 lens magnifications. We designed the hDL-system, employing the VGG16 pre-trained convolution neural network for generating DL-features, the SVM classifier, and the bootstrap evaluation method for assessing the discrimination accuracy between low-grade and high-grade CRC lesions. Furthermore, we compared the hDL-system’s discrimination accuracy with that of a supervised machine learning system (sML-system). We designed the sML-system by (i) generating sixty-nine (69) textural and colour features from each image, (ii) employing the probabilistic neural network (PNN) classifier, and (iii) using the bootstrapping method for evaluating sML-system performance. The system design was enabled by employing the CUDA platform for programming in parallel the multiprocessors of the Nvidia graphics processing unit card. The hDL-system provided the highest discrimination accuracy of 99.1% using the × 200 lens magnification images as compared to the 92.5.% best accuracy achieved by the sML-system, employing both the × 50 and × 200 lens magnification images. Our results showed that the hDL-system was superior to the sML-system (i) in discriminating low-grade from high-grade CRC-lesions and (ii) by requiring fewer images for its best design, only those at the × 200 lens magnification. The sML-system by employing textural and colour features in its design revealed that high-grade CRC lesions are characterized by (i) loss in the definition of structures, (ii) coarser texture in larger structures, (iii) hazy formless texture, (iv) lower AIB1 uptake, (v) lower local correlation and (vi) slower varying image contrast.