arXiv:2411.16885v1 Announce Type: new
Abstract: In recent years, the use of deep learning (DL) methods, including convolutional neural networks (CNNs) and vision transformers (ViTs), has significantly advanced computational pathology, enhancing both diagnostic accuracy and efficiency. Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) plays a crucial role by providing detailed tissue samples for the analysis and training of DL models. However, WSIs often contain regions with artifacts such as tissue folds, blurring, as well as non-tissue regions (background), which can negatively impact DL model performance. These artifacts are diagnostically irrelevant and can lead to inaccurate results. This paper proposes a fully automatic supervised DL pipeline for WSI Quality Assessment (WSI-QA) that uses a fused model combining CNNs and ViTs to detect and exclude WSI regions with artifacts, ensuring that only qualified WSI regions are used to build DL-based computational pathology applications. The proposed pipeline employs a pixel-based segmentation model to classify WSI regions as either qualified or non-qualified based on the presence of artifacts. The proposed model was trained on a large and diverse dataset and validated with internal and external data from various human organs, scanners, and H&E staining procedures. Quantitative and qualitative evaluations demonstrate the superiority of the proposed model, which outperforms state-of-the-art methods in WSI artifact detection. The proposed model consistently achieved over 95% accuracy, precision, recall, and F1 score across all artifact types. Furthermore, the WSI-QA pipeline shows strong generalization across different tissue types and scanning conditions.
Analysis of the Content
The content of this article discusses the use of deep learning (DL) methods, specifically convolutional neural networks (CNNs) and vision transformers (ViTs), in computational pathology. The focus is on the quality assessment of Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) and the detection and exclusion of regions with artifacts. The article proposes a fully automatic supervised DL pipeline that combines CNNs and ViTs to ensure only qualified WSI regions are used for DL-based computational pathology applications.
One of the key points raised in this article is the importance of accurate and efficient computational pathology. DL methods have significantly advanced the field, and the use of CNNs and ViTs in this context shows the multi-disciplinary nature of the concepts discussed. DL techniques from the field of computer vision are applied to the analysis of medical images, specifically WSIs, which are essential for training DL models. This intersection of computer vision and medical imaging highlights the broader field of multimedia information systems, where the processing and analysis of various types of media data, such as images and videos, are essential for decision-making in different domains.
Another important aspect emphasized in the article is the impact of artifacts in WSIs on DL model performance. The presence of artifacts, such as tissue folds, blurring, and non-tissue regions, can lead to inaccurate results and affect the diagnostic accuracy of computational pathology applications. Hence, detecting and excluding these artifacts is crucial. The proposed DL pipeline tackles this challenge by employing a pixel-based segmentation model to classify WSI regions as qualified or non-qualified based on the presence of artifacts. This approach demonstrates the integration of image segmentation techniques into DL pipelines, further highlighting the multi-disciplinary nature of the concepts discussed.
The evaluation results presented in the article demonstrate the superiority of the proposed DL model for artifact detection in WSIs. With consistently high accuracy, precision, recall, and F1 score across all artifact types, the model outperforms state-of-the-art methods in this domain. Additionally, the strong generalization of the WSI-QA pipeline across different tissue types and scanning conditions further highlights the potential impact of this research in the field of computational pathology.
Relation to Multimedia Information Systems and Virtual Realities
The concepts discussed in this article directly relate to the wider field of multimedia information systems. WSIs are a form of multimedia data generated in medical imaging, and their accurate analysis and interpretation are crucial for decision-making in pathology. The application of DL methods in this context shows how multimedia information systems can be enhanced and leveraged to improve diagnostic accuracy and efficiency in medicine. Furthermore, the integration of image segmentation models and DL pipelines demonstrates the multi-disciplinary nature of multimedia information systems, where techniques from computer vision and machine learning are combined for enhanced analysis and interpretation of multimedia data.
The content also has relevance to the domains of virtual realities and augmented reality. As virtual reality and augmented reality technologies continue to advance, the integration of DL methods for the analysis of medical images, such as WSIs, can contribute to the development of immersive and interactive medical visualization systems. By ensuring the quality of WSIs and excluding regions with artifacts, DL models can provide more accurate representations of tissue samples in virtual or augmented reality environments. This integration of DL with virtual and augmented realities has the potential to revolutionize the way pathologists and medical professionals interact with and interpret medical images, enhancing both the accuracy and efficiency of diagnostic processes.