Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis

• URL: http://arxiv.org/abs/2407.19512v1
• Date: Sun, 28 Jul 2024 15:29:07 GMT
• Title: Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis
• Authors: Honglin Li, Yusuan Sun, Chenglu Zhu, Yunlong Zhang, Shichuan Zhang, Zhongyi Shui, Pingyi Chen, Jingxiong Li, Sunyi Zheng, Can Cui, Lin Yang,
• Abstract summary: Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via Whole Slide Image (WSI) diagnosis is critical to prevent this Cancer progression and improve survival rate. But pathologist's single test suffers inevitable false negative due to the immense number of cells that need to be reviewed within a WSI.
• Score: 11.148919818020495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via cytology Whole Slide Image (WSI) diagnosis is critical to prevent this Cancer progression and improve survival rate, but pathologist's single test suffers inevitable false negative due to the immense number of cells that need to be reviewed within a WSI. Though computer-aided automated diagnostic models can serve as strong complement for pathologists, their effectiveness is hampered by the paucity of extensive and detailed annotations, coupled with the limited interpretability and robustness. These factors significantly hinder their practical applicability and reliability in clinical settings. To tackle these challenges, we develop an AI approach, which is a Scalable Technology for Robust and Interpretable Diagnosis built on Extensive data (STRIDE) of cervical cytology. STRIDE addresses the bottleneck of limited annotations by integrating patient-level labels with a small portion of cell-level labels through an end-to-end training strategy, facilitating scalable learning across extensive datasets. To further improve the robustness to real-world domain shifts of cytology slide-making and imaging, STRIDE employs color adversarial samples training that mimic staining and imaging variations. Lastly, to achieve pathologist-level interpretability for the trustworthiness in clinical settings, STRIDE can generate explanatory textual descriptions that simulates pathologists' diagnostic processes by cell image feature and textual description alignment. Conducting extensive experiments and evaluations in 183 medical centers with a dataset of 341,889 WSIs and 0.1 billion cells from cervical cytology patients, STRIDE has demonstrated a remarkable superiority over previous state-of-the-art techniques.

Related papers

• TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis [3.262230127283452]
  Topological data analysis offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. We show that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.
  arXiv  Detail & Related papers  (2024-10-13T12:24:13Z)
• Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
  We propose a novel text-guided learning method to achieve highly accurate cancer detection results. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
  arXiv  Detail & Related papers  (2024-05-23T07:03:38Z)
• Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification [0.0]
  Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks. Histopathology remains the gold standard, although time-consuming and vulnerable to inter-observer mistakes. Recent advances in deep learning have generated interest in its application to medical imaging analysis.
  arXiv  Detail & Related papers  (2024-05-07T18:49:34Z)
• Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images [2.232567376976564]
  Since 2019, the global dissemination of the Coronavirus and its novel strains has resulted in a surge of new infections. The use of X-ray and computed tomography (CT) imaging techniques is critical in diagnosing and managing COVID-19. This paper focuses on the methodological approach of using machine learning (ML) to enhance medical imaging for COVID-19 diagnosis.
  arXiv  Detail & Related papers  (2024-01-13T09:17:39Z)
• SSASS: Semi-Supervised Approach for Stenosis Segmentation [9.767759441883008]
  The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task. We introduce a semi-supervised approach for cardiovascular stenosis segmentation. Our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics.
  arXiv  Detail & Related papers  (2023-11-17T02:01:19Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance [6.281092892485014]
  Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system. Since adopting the current standard-of-care treatment 18 years ago, no substantial prognostic improvement has been noticed. Here, we focus on identifying prognostically relevant characteristics from H&E stained WSI & clinical data relating to OS.
  arXiv  Detail & Related papers  (2023-02-01T18:56:09Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.