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.

要设计一个混合深度学习系统（hDL系统），使用免疫组织化学染色的活检标本对AIB1表达进行区分，以区分低度结直肠癌（CRC）病变。AIB1在肿瘤发生中具有致癌作用，并且它是关于包括CRC在内的各种类型癌症的重要预后因素。临床材料包括67例经证实的CRC的活检标本（26例低级别，41例高级别病例）。对于每位患者，我们将镜头放大倍数分别为×50和×200进行数字化处理。我们设计了hDL系统，采用VGG16预训练卷积神经网络生成DL特征，SVM分类器和自举评估方法来评估低度和高度CRC病变之间的鉴别准确性。此外，我们将hDL系统的辨别准确性与监督式机器学习系统（sML-system）的辨别准确性进行了比较。我们设计了sML系统，方法是：（i）从每个图像生成六十九（69）个纹理和颜色特征，（ii）使用概率神经网络（PNN）分类器，并且（iii）使用自举法评估sML-系统性能。通过采用CUDA平台对Nvidia图形处理单元卡的多处理器进行并行编程，可以实现系统设计。与使用50倍和200倍镜头放大图像的sML系统相比，hDL系统在使用200倍镜头放大倍率图像时可提供99.1％的最高识别精度。我们的结果表明，hDL系统优于sML系统（i）在区分低级和高级CRC病变方面，以及（ii）只需较少的图像即可获得最佳设计，仅使用×200镜头放大。通过在设计中采用纹理和颜色特征的sML系统显示，高等级CRC病变的特征是（i）结构定义缺失，（ii）较大结构中的粗糙纹理，（iii）朦胧的无形纹理，（ iv）较低的AIB1摄取量，（v）较低的局部相关性，以及（vi）较慢的变化图像对比度。