Tutorial on the development of AI models for medical image analysis
- URL: http://arxiv.org/abs/2208.00766v1
- Date: Thu, 14 Jul 2022 11:21:19 GMT
- Title: Tutorial on the development of AI models for medical image analysis
- Authors: Thijs Kooi
- Abstract summary: The idea of using computers to read medical scans was introduced as early as 1966.
The Alexnet breakthrough in 2012 sparked new interest in the topic.
In spite of success for some diseases and modalities, many challenges remain.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The idea of using computers to read medical scans was introduced as early as
1966. However, limits to machine learning technology meant progress was slow
initially. The Alexnet breakthrough in 2012 sparked new interest in the topic,
which resulted in the release of 100s of medical AI solutions on the market. In
spite of success for some diseases and modalities, many challenges remain.
Research typically focuses on the development of specific applications or
techniques, clinical evaluation, or meta analysis of clinical studies or
techniques through surveys or challenges. However, limited attention has been
given to the development process of improving real world performance. In this
tutorial, we address the latter and discuss some techniques to conduct the
development process in order to make this as efficient as possible.
Related papers
- 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) - 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) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in
Healthcare with Automated Machine Learning [72.2614468437919]
We present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models.
We provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank.
Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide.
arXiv Detail & Related papers (2022-10-21T16:31:46Z) - Nuclei & Glands Instance Segmentation in Histology Images: A Narrative
Review [0.5893124686141781]
Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow.
With the advent of modern hardware, the recent availability of large-scale quality public datasets and organized grand challenges have seen a surge in automated methods.
In this survey, 126 papers illustrating the AI based methods for nuclei and glands segmentation published in the last five years-2022) are deeply analyzed.
arXiv Detail & Related papers (2022-08-26T06:52:15Z) - Levels of Autonomous Radiology [0.0]
The development and adoption of Artificial Intelligence (AI) applications using medical data will lead to the next phase of evolution in radiology.
It will include automating laborious manual tasks such as annotations, report-generation, etc., along with the initial radiological assessment of cases to aid radiologists in their evaluation workflow.
We propose a level-wise classification for the progression of automation in radiology, explaining AI assistance at each level with corresponding challenges and solutions.
arXiv Detail & Related papers (2021-12-14T10:41:56Z) - A Review of Artificial Intelligence Technologies for Early Prediction of
Alzheimer's Disease [1.1650381752104297]
Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and brain functions.
The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms.
arXiv Detail & Related papers (2020-12-22T01:05:34Z) - Achievements and Challenges in Explaining Deep Learning based
Computer-Aided Diagnosis Systems [4.9449660544238085]
We discuss early achievements in development of explainable AI for validation of known disease criteria.
We highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool.
arXiv Detail & Related papers (2020-11-26T08:08:19Z) - 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) - 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)
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.