Radiomics analysis in medical imaging research

Elmahdy, Mahmoud; Sebro, Ronnie

doi:10.1002/jmrs.662

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…This can be achieved by working towards the responsible development and use of AI, and fostering a collaborative effort among stakeholders, including experts, policymakers, and the public, to ensure that AI is harnessed for the greater good. Perhaps, an FDA like regulation for Large Language Models, beyond GPT-4 can be one potential solution [172].…”

Section: B Consideration Of a 6-month Suspension: Is It Necessary?mentioning

confidence: 99%

A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

Hadi,

tashi,

Qureshi

et al. 2023

Preprint

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<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. Large language models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, and question-answering. This survey paper provides a comprehensive overview of LLMs, including their history, architecture, training methods, applications, and challenges. The paper begins by discussing the fundamental concepts of generative AI and the architecture of generative pre- trained transformers (GPT). It then provides an overview of the history of LLMs, their evolution over time, and the different training methods that have been used to train them. The paper then discusses the wide range of applications of LLMs, including medical, education, finance, and engineering. It also discusses how LLMs are shaping the future of AI and how they can be used to solve real-world problems. The paper then discusses the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, and computational resource requirements. It also highlights techniques for enhancing the robustness and controllability of LLMs, and addressing bias, fairness, and generation quality issues. Finally, the paper concludes by highlighting the future of LLM research and the challenges that need to be addressed in order to make LLMs more reliable and useful. This survey paper is intended to provide researchers, practitioners, and enthusiasts with a comprehensive understanding of LLMs, their evolution, applications, and challenges. By consolidating the state-of-the-art knowledge in the field, this survey serves as a valuable resource for further advancements in the development and utilization of LLMs for a wide range of real-world applications. The GitHub repo for this project is available at https://github.com/anas-zafar/LLM-Survey</p>

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Section: B Consideration Of a 6-month Suspension: Is It Necessary?mentioning

confidence: 99%

A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

Hadi,

tashi,

Qureshi

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, AI image interpretation is consistent, fast, and at least of similar accuracy to human reporting, even in studies performed from an 'AI-hostile' point of view [23] leading to the opinion that 'we should stop training radiologists now'. [24] AI also makes radiomics possible, i.e., the extraction of quantitative data from an imaging dataset; [25] early and already widely used applications include longitudinal strain in echo and T1 mapping in cardiac magnetic resonance (CMR), but the potential of this type of 'deepdive' into data mining is much broader and not yet fully realised.…”

Section: Impact Of Ai On Cardiac Imagingmentioning

confidence: 99%

The Devastating Impact of Artificial Intelligence (AI) on the Traditional Ways of Practising Imaging and Cardiology Are ‘imagers’ an endangered species, and should trainees rethink their careers?

Protty,

Ionescu

2023

Romanian Journal of Cardiology

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“…Recent studies have affirmed the feasibility of incorporating radiomics into ultrasound imaging with noteworthy findings [13][14][15]. Radiomics stands out as a robust tool for extracting features from images, encompassing elements like texture, pixel intensities, and other critical attributes [16,17].…”

Section: Introductionmentioning

confidence: 97%

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

Pathour,

Ma,

Strand

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

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Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using ex-vivo whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.

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Radiomics analysis in medical imaging research

Cited by 6 publications

References 40 publications

A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

The Devastating Impact of Artificial Intelligence (AI) on the Traditional Ways of Practising Imaging and Cardiology Are ‘imagers’ an endangered species, and should trainees rethink their careers?

Feature extraction of ultrasound radiofrequency data for the classification of the peripheral zone of human prostate

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