Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey

• URL: http://arxiv.org/abs/2311.10118v1
• Date: Thu, 16 Nov 2023 09:20:43 GMT
• Title: Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey
• Authors: Zhu Meng, Junhao Dong, Limei Guo, Fei Su, Guangxi Wang, Zhicheng Zhao
• Abstract summary: Signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival. Deep learning has received increasing attention to assist pathologists in improving the diagnostic efficiency and accuracy. This paper provides a survey on SRC analysis driven by deep learning from 2008 to August 2023.
• Score: 24.605310211285392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists. Although automatic diagnosis SRCs based on deep learning has received increasing attention to assist pathologists in improving the diagnostic efficiency and accuracy, the existing works have not been systematically overviewed, which hindered the evaluation of the gap between algorithms and clinical applications. In this paper, we provide a survey on SRC analysis driven by deep learning from 2008 to August 2023. Specifically, the biological characteristics of SRCs and the challenges of automatic identification are systemically summarized. Then, the representative algorithms are analyzed and compared via dividing them into classification, detection, and segmentation. Finally, for comprehensive consideration to the performance of existing methods and the requirements for clinical assistance, we discuss the open issues and future trends of SRC analysis. The retrospect research will help researchers in the related fields, particularly for who without medical science background not only to clearly find the outline of SRC analysis, but also gain the prospect of intelligent diagnosis, resulting in accelerating the practice and application of intelligent algorithms.

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)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• 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)
• Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy [43.55994393060723]
  gaitXplorer is a visual analytics approach for the classification of CP-related gait patterns. It integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications.
  arXiv  Detail & Related papers  (2022-08-10T09:21:28Z)
• Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia [5.64692772904991]
  We propose a sparse WSI analysis method for the rapid identification of high-power ROI for WSI-level classification. We test our method on a common but time-consuming task in pathology - that of diagnosing gastric intestinal metaplasia (GIM) on hematoxylin and eosin slides. Our method successfully detects GIM in all positive WSI, with a WSI-level classification area under the receiver operating characteristic curve (AUC) of 0.98 and an average precision (AP) of 0.95.
  arXiv  Detail & Related papers  (2022-01-05T04:43:46Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering [1.52292571922932]
  This study proposes a feature extraction algorithm based on group intelligence which we call a Plant Root System (PRS) algorithm. It is expected that more biomedical signals can be applied to clinical diagnosis using the proposed algorithm.
  arXiv  Detail & Related papers  (2021-07-31T11:00:32Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• 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)

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.