Responsible Deep Learning for Software as a Medical Device

• URL: http://arxiv.org/abs/2312.13333v1
• Date: Wed, 20 Dec 2023 18:12:07 GMT
• Title: Responsible Deep Learning for Software as a Medical Device
• Authors: Pratik Shah, Jenna Lester, Jana G Deflino, Vinay Pai
• Abstract summary: This extended version of the workshop paper was presented at the special session of the 2022 IEEE 19th International Symposium on Biomedical Imaging. It describes strategy and opportunities by University of California professors engaged in machine learning and clinical research. Performance evaluations of AI/ML models of skin (RGB), tissue biopsy (digital pathology), and lungs and kidneys (Magnetic Resonance, X-ray, Computed Tomography) medical images for regulatory evaluations and real-world deployment are discussed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Tools, models and statistical methods for signal processing and medical image analysis and training deep learning models to create research prototypes for eventual clinical applications are of special interest to the biomedical imaging community. But material and optical properties of biological tissues are complex and not easily captured by imaging devices. Added complexity can be introduced by datasets with underrepresentation of medical images from races and ethnicities for deep learning, and limited knowledge about the regulatory framework needed for commercialization and safety of emerging Artificial Intelligence (AI) and Machine Learning (ML) technologies for medical image analysis. This extended version of the workshop paper presented at the special session of the 2022 IEEE 19th International Symposium on Biomedical Imaging, describes strategy and opportunities by University of California professors engaged in machine learning (section I) and clinical research (section II), the Office of Science and Engineering Laboratories (OSEL) section III, and officials at the US FDA in Center for Devices & Radiological Health (CDRH) section IV. Performance evaluations of AI/ML models of skin (RGB), tissue biopsy (digital pathology), and lungs and kidneys (Magnetic Resonance, X-ray, Computed Tomography) medical images for regulatory evaluations and real-world deployment are discussed.

Related papers

• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions [0.0]
  Recent breakthroughs in deep learning, a subset of artificial intelligence, have markedly revolutionized the analysis of medical pictures. CNNs have demonstrated remarkable proficiency in autonomously learning features from multidimensional medical pictures. These models have been utilized across multiple medical disciplines, including pathology, radiology, ophthalmology, and cardiology.
  arXiv  Detail & Related papers  (2024-10-18T02:57:14Z)
• Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis [17.4235794108467]
  The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes.
  arXiv  Detail & Related papers  (2024-03-26T09:55:49Z)
• AutoML Systems For Medical Imaging [5.581919089808456]
  The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. An automated machine learning approach simplifies the creation of custom image recognition models. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes.
  arXiv  Detail & Related papers  (2023-06-07T19:57:07Z)
• A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
  TRUDLMIA is a trustworthy deep learning framework for medical image analysis. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
  arXiv  Detail & Related papers  (2022-12-06T05:30:22Z)
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A survey [89.45221564651145]
  This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications. It covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets.
  arXiv  Detail & Related papers  (2022-11-10T13:37:36Z)
• Computer-Assisted Analysis of Biomedical Images [1.0116577992023341]
  This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis. The ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies.
  arXiv  Detail & Related papers  (2021-06-04T21:59:48Z)
• Medical Imaging and Machine Learning [16.240472115235253]
  The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging. Data availability, need for novel computing architectures and explainable AI algorithms, are still relevant. In this paper we explore challenges unique to high dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations.
  arXiv  Detail & Related papers  (2021-03-02T18:53:39Z)
• 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)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)
• 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.