Medical Imaging and Machine Learning

• URL: http://arxiv.org/abs/2103.01938v1
• Date: Tue, 2 Mar 2021 18:53:39 GMT
• Title: Medical Imaging and Machine Learning
• Authors: Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger
• Abstract summary: 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.
• Score: 16.240472115235253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios. The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization, and translatable clinical decision support systems. Among the key issues raised in the report: data availability, need for novel computing architectures and explainable AI algorithms, are still relevant despite the tremendous progress made over the past few years alone. Furthermore, translational goals of data sharing, validation of performance for regulatory approval, generalizability and mitigation of unintended bias must be accounted for early in the development process. In this perspective paper we explore challenges unique to high dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations in developing high-dimensional, multi-modality, machine learning systems for clinical decision support.

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)
• Artificial intelligence techniques in inherited retinal diseases: A review [19.107474958408847]
  Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs.
  arXiv  Detail & Related papers  (2024-10-10T03:14:51Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• 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)
• Responsible Deep Learning for Software as a Medical Device [0.0]
  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.
  arXiv  Detail & Related papers  (2023-12-20T18:12:07Z)
• 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)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• 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)
• Recent advances and clinical applications of deep learning in medical image analysis [7.132678647070632]
  We reviewed and summarized more than 200 recently published papers to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical images.
  arXiv  Detail & Related papers  (2021-05-27T18:05:12Z)
• 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)
• 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)

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.