In the domain of image processing, several research problems and gaps continuously emerged and always there is a need for novel and efficient techniques. Based on this domain, we suggest a few major research problems and gaps which could inspire you to investigate in your research:
- Data Heterogeneity and Integration
- Problem: There are various types of medical imaging data such as ultrasound, CT, and MRT. Every type has diverse resolutions and features.
- Research Gap: For extensive analysis, still there is a requirement for efficient techniques in order to combine these kinds of data into a coherent structure.
- Advanced Machine Learning Models
- Problem: Mostly, conventional machine learning frameworks confront challenges with medical images due to its nature of large size and complexness.
- Research Gap: In medical images, managing complicated patterns with more credibility and preciseness is very crucial. For that, the creation of highly advanced deep learning frameworks is required.
- Interpretability and Explainability
- Problem: Specifically in medical applications, deep learning models are considered as black boxes, because of this, it adds complexity to clinical approvals.
- Research Gap: Creation of highly explainable and understandable AI frameworks is most significant for offering perceptions based on the decision-making procedures. But, research is still required for this creation process.
- Real-time Processing
- Problem: For several medical applications, the actual-time image analysis is more important. But at the same time, it is examined as difficult in terms of computation.
- Research Gap: To attain progression, enhancing methods while preserving preciseness is considered as essential for actual-time analysis.
- Data Privacy and Security
- Problem: Major safety and confidentiality issues are increased in using medical images because of having vulnerable personal details.
- Research Gap: For securing patient confidentiality, it is crucial to have safer and powerful techniques to process and distribute clinical images.
- Quantitative Imaging Biomarkers
- Problem: Particularly for disease identification and prediction, the process of creating credible biomarkers from image data remains incomplete.
- Research Gap: As a means to retrieve and verify novel imaging biomarkers, exploration based on automatic tools is still required.
- Multi-modal Image Fusion
- Problem: Various details are offered by each imaging type, so integration of these types is technically challenging, even though it is robust and effective.
- Research Gap: To enhance diagnostic preciseness, integrating details from several types of images is important. But, the required approaches for integration are insufficient.
- Generalization Across Different Institutions
- Problem: In most of the cases, the AI model does not effectively generalize to data from other institutions, especially when it is trained on data from one institution.
- Research Gap: The major research requirement is to create models which are capable of generalizing among various populations and platforms in an efficient manner.
How do I find medical research articles?
The process of searching appropriate articles for research work is examined as important as well as interesting. It is significant to follow several guidelines to discover suitable articles. The following are procedural instructions that assist you to identify research articles relevant to medical domain:
- Database Chosen
For discovering scientific and medical-based literature, select the appropriate databases. The following are a few famous databases that are more suitable for searching literatures:
- PubMed: It is a freely available resource which has various collections of articles relevant to healthcare and medicine.
- Scopus and Web of Science: These databases offer articles related to broader scientific domains by including extensive analysis.
- Google Scholar: Based on the collection of domains or published copies, this database includes complete text or metadata of academic literature. It is generally a web search engine that is openly available.
- EMBASE: This database encompasses details in an extensive manner on the basis of pharmaceutical exploration.
- Cochrane Library: Relevant to the domain of healthcare, it has systematic reviews.
- Utilizing Search Terms Efficiently
- Key Terms: Regarding your topic, create a collection of appropriate key terms. It is important to examine relevant terms, differences in wordings, and synonyms.
- Boolean Operators: To enhance your search, utilize operators such as AND, OR, and, NOT. As an instance: it is easier to get articles based on utilization of MRI in brain tumors by searching “MRI AND brain tumor”.
- Wildcards and Truncation: For identifying extensions of a terminology, employ symbols such as asterisk (*). As an example: you can obtain suggestions like neuron, neurological, neurology, etc by exploring “neuro*”.
- Innovative Search Functionality
Some innovative search choices are provided by several databases in terms of various aspects like language, kind of publication, article type, and others, in which researchers can confine their search process. To compress your outcomes in an efficient manner, utilize these functionalities.
- Reading Abstracts and Papers
- Abstracts: To decide whether the article aligns with the requirements of your study, read the abstract section before exploring the entire article.
- Accessing Entire Texts: It is advisable to verify whether the library of your university offers access permission to articles by means of subscriptions, specifically if the entire text is not openly available. Contrary to that, you can demand entire texts from the authors straightly by using tools such as Academia.edu or ResearchGate.
PhD In Medical Image Processing Topics & Ideas
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