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Prezja F, Annala L, Kiiskinen S, Lahtinen S, Ojala T, Ruusuvuori P, Kuopio T. Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning. Heliyon 2024; 10:e37561. [PMID: 39309850 PMCID: PMC11415691 DOI: 10.1016/j.heliyon.2024.e37561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2024] [Accepted: 09/05/2024] [Indexed: 09/25/2024] Open
Abstract
In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) to facilitate the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of CNNs to accurately classify diverse tissue types from whole slide microscope images. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid deep transfer learning and ensemble machine learning model that improves upon previous approaches, including a transformer and neural architecture search baseline for this task. We employed a pairing of the EfficientNetV2 architecture with a random forest classification head. Our model achieved 96.74% accuracy (95% CI: 96.3%-97.1%) on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in the task, we have made them publicly available.
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Affiliation(s)
- Fabi Prezja
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Leevi Annala
- University of Helsinki, Faculty of Science, Department of Computer Science, Helsinki, Finland
- University of Helsinki, Faculty of Agriculture and Forestry, Department of Food and Nutrition, Helsinki, Finland
| | - Sampsa Kiiskinen
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Suvi Lahtinen
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
- University of Jyväskylä, Faculty of Mathematics and Science, Department of Biological and Environmental Science, Jyväskylä, 40014, Finland
| | - Timo Ojala
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Pekka Ruusuvuori
- University of Turku, Institute of Biomedicine, Cancer Research Unit, Turku, 20014, Finland
- Turku University Hospital, FICAN West Cancer Centre, Turku, 20521, Finland
| | - Teijo Kuopio
- University of Jyväskylä, Department of Biological and Environmental Science, Jyväskylä, 40014, Finland
- Hospital Nova of Central Finland, Department of Pathology, Jyväskylä, 40620, Finland
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Prezja F, Äyrämö S, Pölönen I, Ojala T, Lahtinen S, Ruusuvuori P, Kuopio T. Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions. Sci Rep 2023; 13:15879. [PMID: 37741820 PMCID: PMC10517936 DOI: 10.1038/s41598-023-42357-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined 'Deep Stroma') depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model's limitations and capabilities.
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Affiliation(s)
- Fabi Prezja
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland.
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland.
| | - Sami Äyrämö
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Ilkka Pölönen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Spectral Imaging Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Timo Ojala
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Suvi Lahtinen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Department of Biological and Environmental Science, Faculty of Mathematics and Science, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Pekka Ruusuvuori
- Institute of Biomedicine, Cancer Research Unit, University of Turku, Turku, 20014, Finland
- FICAN West Cancer Centre, Turku University Hospital, Turku, 20521, Finland
| | - Teijo Kuopio
- Department of Education and Research, Hospital Nova of Central Finland, Jyväskylä, 40620, Finland
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, 40014, Finland
- Department of Pathology, Hospital Nova of Central Finland, Jyväskylä, 40620, Finland
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Hassan T, Shafay M, Hassan B, Akram MU, ElBaz A, Werghi N. Knowledge distillation driven instance segmentation for grading prostate cancer. Comput Biol Med 2022; 150:106124. [PMID: 36208597 DOI: 10.1016/j.compbiomed.2022.106124] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 08/29/2022] [Accepted: 09/17/2022] [Indexed: 11/27/2022]
Abstract
Prostate cancer (PCa) is one of the deadliest cancers in men, and identifying cancerous tissue patterns at an early stage can assist clinicians in timely treating the PCa spread. Many researchers have developed deep learning systems for mass-screening PCa. These systems, however, are commonly trained with well-annotated datasets in order to produce accurate results. Obtaining such data for training is often time and resource-demanding in clinical settings and can result in compromised screening performance. To address these limitations, we present a novel knowledge distillation-based instance segmentation scheme that allows conventional semantic segmentation models to perform instance-aware segmentation to extract stroma, benign, and the cancerous prostate tissues from the whole slide images (WSI) with incremental few-shot training. The extracted tissues are then used to compute majority and minority Gleason scores, which, afterward, are used in grading the PCa as per the clinical standards. The proposed scheme has been thoroughly tested on two datasets, containing around 10,516 and 11,000 WSI scans, respectively. Across both datasets, the proposed scheme outperforms state-of-the-art methods by 2.01% and 4.45%, respectively, in terms of the mean IoU score for identifying prostate tissues, and 10.73% and 11.42% in terms of F1 score for grading PCa according to the clinical standards. Furthermore, the applicability of the proposed scheme is tested under a blind experiment with a panel of expert pathologists, where it achieved a statistically significant Pearson correlation of 0.9192 and 0.8984 with the clinicians' grading.
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Affiliation(s)
- Taimur Hassan
- KUCARS and C2PS, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788, United Arab Emirates; Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan.
| | - Muhammad Shafay
- KUCARS and C2PS, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788, United Arab Emirates
| | - Bilal Hassan
- KUCARS and C2PS, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788, United Arab Emirates; School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China
| | - Muhammad Usman Akram
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan
| | - Ayman ElBaz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA
| | - Naoufel Werghi
- KUCARS and C2PS, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, 127788, United Arab Emirates
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Construction of Safety Early Warning Model for Construction of Engineering Based on Convolution Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8937084. [PMID: 36156978 PMCID: PMC9507696 DOI: 10.1155/2022/8937084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/12/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022]
Abstract
In recent years, China’s engineering construction management level has been greatly improved, but compared with other industries, the construction industry still has low production efficiency, serious waste, and low level of information problems, and especially in the process of engineering management practice, schedule delay has become the focus of engineering management problems. With the continuous development of science and technology, computer and information technology have been continuously applied in engineering, among which deep neural network (DNN) technology, lean management, information visualization technology, and other technologies have become the hot spot of industry research, and the application of these emerging technologies to improve the level of project schedule control has become an urgent demand of the industry. Therefore, on the basis of deep learning, this paper analyzes the principle and application of object detection and feature extraction constructed by neural network and combines text feature extraction and image feature extraction methods. This application provides a new idea for the development of the construction industry.
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