Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images
Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each year, and an accurate diagnosis requires a signif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/11/398 |
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| author | Sergey A. Soldatov Danil M. Pashkov Sergey A. Guda Nikolay S. Karnaukhov Alexander A. Guda Alexander V. Soldatov |
| author_facet | Sergey A. Soldatov Danil M. Pashkov Sergey A. Guda Nikolay S. Karnaukhov Alexander A. Guda Alexander V. Soldatov |
| author_sort | Sergey A. Soldatov |
| collection | DOAJ |
| description | Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each year, and an accurate diagnosis requires a significant amount of time and a highly qualified pathologist to decrease the high mortality rate. Recent development of artificial intelligence technologies and scanning microscopy introduced digital pathology into the field of cancer diagnosis by means of the whole-slide image (WSI). In this work, we applied deep learning methods to diagnose six types of colon mucosal lesions using convolutional neural networks (CNNs). As a result, an algorithm for the automatic segmentation of WSIs of colon biopsies was developed, implementing pre-trained, deep convolutional neural networks of the ResNet and EfficientNet architectures. We compared the classical method and one-cycle policy for CNN training and applied both multi-class and multi-label approaches to solve the classification problem. The multi-label approach was superior because some WSI patches may belong to several classes at once or to none of them. Using the standard one-vs-rest approach, we trained multiple binary classifiers. They achieved the receiver operator curve AUC in the range of 0.80–0.96. Other metrics were also calculated, such as accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score. Obtained CNNs can support human pathologists in the diagnostic process and can be extended to other cancers after adding a sufficient amount of labeled data. |
| first_indexed | 2024-03-09T19:20:44Z |
| format | Article |
| id | doaj.art-1f2188b4b0b8455a95fc04a555b7bfbe |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T19:20:44Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-1f2188b4b0b8455a95fc04a555b7bfbe2023-11-24T03:22:51ZengMDPI AGAlgorithms1999-48932022-10-01151139810.3390/a15110398Deep Learning Classification of Colorectal Lesions Based on Whole Slide ImagesSergey A. Soldatov0Danil M. Pashkov1Sergey A. Guda2Nikolay S. Karnaukhov3Alexander A. Guda4Alexander V. Soldatov5The Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, RussiaMoscow Clinical Scientific Center n.a. A.S. Loginov, 111123 Moscow, RussiaThe Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 344090 Rostov-on-Don, RussiaMicroscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each year, and an accurate diagnosis requires a significant amount of time and a highly qualified pathologist to decrease the high mortality rate. Recent development of artificial intelligence technologies and scanning microscopy introduced digital pathology into the field of cancer diagnosis by means of the whole-slide image (WSI). In this work, we applied deep learning methods to diagnose six types of colon mucosal lesions using convolutional neural networks (CNNs). As a result, an algorithm for the automatic segmentation of WSIs of colon biopsies was developed, implementing pre-trained, deep convolutional neural networks of the ResNet and EfficientNet architectures. We compared the classical method and one-cycle policy for CNN training and applied both multi-class and multi-label approaches to solve the classification problem. The multi-label approach was superior because some WSI patches may belong to several classes at once or to none of them. Using the standard one-vs-rest approach, we trained multiple binary classifiers. They achieved the receiver operator curve AUC in the range of 0.80–0.96. Other metrics were also calculated, such as accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score. Obtained CNNs can support human pathologists in the diagnostic process and can be extended to other cancers after adding a sufficient amount of labeled data.https://www.mdpi.com/1999-4893/15/11/398deep learningconvolutional neural networkswhole-slide imagedigital pathologycolon cancer |
| spellingShingle | Sergey A. Soldatov Danil M. Pashkov Sergey A. Guda Nikolay S. Karnaukhov Alexander A. Guda Alexander V. Soldatov Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images Algorithms deep learning convolutional neural networks whole-slide image digital pathology colon cancer |
| title | Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images |
| title_full | Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images |
| title_fullStr | Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images |
| title_full_unstemmed | Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images |
| title_short | Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images |
| title_sort | deep learning classification of colorectal lesions based on whole slide images |
| topic | deep learning convolutional neural networks whole-slide image digital pathology colon cancer |
| url | https://www.mdpi.com/1999-4893/15/11/398 |
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