Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics

• URL: http://arxiv.org/abs/2110.10332v1
• Date: Wed, 20 Oct 2021 01:05:47 GMT
• Title: Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics
• Authors: Fereshteh Yousefirizi, Pierre Decasez, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
• Abstract summary: This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches. Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors.
• Score: 2.2509387878255818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of radiomics and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

Related papers

• Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques [38.321248253111776]
  Article explores the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. Aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications.
  arXiv  Detail & Related papers  (2024-06-01T18:50:03Z)
• 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)
• AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices [0.0]
  Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases. This paper reviews recent AI trends and practices in dental CBCT imaging.
  arXiv  Detail & Related papers  (2023-06-05T16:45:39Z)
• Case Studies on X-Ray Imaging, MRI and Nuclear Imaging [0.0]
  We will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images.
  arXiv  Detail & Related papers  (2023-06-03T09:05:35Z)
• Future Artificial Intelligence tools and perspectives in medicine [1.7532045941271799]
  Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. This review explores the progress of AI-based radiomic tools for clinical applications with a brief description of necessary technical steps.
  arXiv  Detail & Related papers  (2022-06-04T11:27:43Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Survey of XAI in digital pathology [3.4591414173342643]
  We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. We give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging. In doing, we incorporate uncertainty estimation methods as an integral part of the XAI landscape.
  arXiv  Detail & Related papers  (2020-08-14T13:11:54Z)
• 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.