Medical Imagery in Case-Based Reasoning

Wilson, David C.; O’Sullivan, Dympna

doi:10.1007/978-3-540-73180-1_13

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Perner (1999) presented a CBR system to identify a degenerative brain disease by using CT images of a patient's brain. Wilson & O'Sullivan (2008) presented a review of medical imagery applications in a CBR system. A CBR system to provide treatment planning in brain cancer radiotherapy was presented by Jagannathan et al (2010).…”

Section: Case-based Reasoning Using Radiographic Imagesmentioning

confidence: 99%

Integrating Radiology into an AI System for Physician Decision Making

Paruchuri¹,

Granville²

2020

CIS

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Over time, physicians gather a vast amount of data such as radiographic images, medical procedures, treatments, and insurance coverage about the patients. A Case-Based Reasoning (CBR) system is developed to organize and retrieve such data to aid physicians in making a diagnosis and formulating a treatment plan for patients with similar traits. This research is an extension of the earlier work presented by the authors. Our earlier research did not consider radiographic images, although radiology plays a vital role in diagnosing and treating patients. Currently, a new algorithm is created to retrieve and organize information from the CBR system to add radiographic images to the patient’s case data. This system is tested and the performance results prove that the acceptance rate achieved by this system is higher than that of the earlier system.

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Section: Case-based Reasoning Using Radiographic Imagesmentioning

confidence: 99%

Integrating Radiology into an AI System for Physician Decision Making

Paruchuri¹,

Granville²

2020

CIS

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“…She proposes using CBR on different levels, mostly focusing on the case representation of images and the similarity measure between cases. A series of works follows this line of research [Perner et al, 2005;Wilson and O'Sullivan, 2008;Perner, 2017]. In a recent survey Perner [2017] reviews applications of CBR in parameter selection, image interpretation, incremental prototype-based classification, novelty detection, and 1-D signal representation.…”

Section: Introductionmentioning

confidence: 99%

Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features

Ye,

Zhao,

Leake

et al. 2021

Preprint

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The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combined process can successfully learn adaptation knowledge applicable to nonsymbolic differences in cases. The CBR system achieves slightly lower performance overall than a baseline deep network regressor, but better performance than the baseline on novel queries.

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“…CBR has been widely adopted both in academic and industrial sectors to solve many problems (e.g. [44,45,48,27,16]). In the early 1990s, CBR was introduced and used to solve spatial and environmental problems.…”

Section: Introductionmentioning

confidence: 99%