The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

• URL: http://arxiv.org/abs/2306.16989v1
• Date: Thu, 29 Jun 2023 14:47:03 GMT
• Title: The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
• Authors: Michael Robben, Amir Hajighasemi, Mohammad Sadegh Nasr, Jai Prakesh Veerla, Anne M. Alsup, Biraaj Rout, Helen H. Shang, Kelli Fowlds, Parisa Boodaghi Malidarreh, Paul Koomey, MD Jillur Rahman Saurav, Jacob M. Luber
• Abstract summary: We identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight.

Related papers

• AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications [0.3937575566991286]
  This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures.
  arXiv  Detail & Related papers  (2025-01-26T11:32:43Z)
• Explainable Artificial Intelligence for Medical Applications: A Review [42.33274794442013]
  This article reviews recent research grounded in explainable artificial intelligence (XAI) It focuses on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.
  arXiv  Detail & Related papers  (2024-11-15T11:31:06Z)
• 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)
• Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images [61.36288157482697]
  Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images.
  arXiv  Detail & Related papers  (2023-11-20T10:45:39Z)
• AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions [3.2071249735671348]
  This review paper summarizes a large collection of articles related to artificial intelligence (AI)-based techniques used in the diagnosis of thyroid cancer. The focus of this study is on how AI-based tools can support the diagnosis and treatment of thyroid cancer, through supervised, unsupervised, or hybrid techniques.
  arXiv  Detail & Related papers  (2023-08-25T17:27:53Z)
• Artificial Intelligence For Breast Cancer Detection: Trends & Directions [0.0]
  This article analyzes different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer.
  arXiv  Detail & Related papers  (2021-10-03T07:22:21Z)
• A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [12.1951719081621]
  Recent advancements in Generative Adrial Networks (GANs) in computer vision may provide a basis for enhanced capabilities in cancer detection and analysis. We assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance. We provide a critical appraisal of the existing literature of GANs applied to cancer imagery, together with suggestions on future research directions to address these challenges.
  arXiv  Detail & Related papers  (2021-07-20T14:57:51Z)
• 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)
• 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.