A fusion method based on Deep Learning and Case-Based Reasoning which improves the resulting medical image segmentations

Corbat, Lisa; Nauval, Mohammad; Henriet, Julien; Lapayre, Jean-Christophe

doi:10.1016/j.eswa.2020.113200

Cited by 23 publications

(6 citation statements)

References 23 publications

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“…In this method, CNNs and CBR are combined to solve the problems of high cost and uneven label quality caused by manual coding and labeling data. Corbat et al (2020) proposed the possibility of using CBR to enhance DL fusion to achieve the fusion of complementary segmentations of scanner images of deformed kidneys and tumors and introduced a hierarchical scheme on the set of cases to be used during the CNN learning stage. This method solves the problem of conflict in segmentation areas when different segmentations overlap.…”

Section: Other Applicationsmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence. In this article, the development of CBR is mainly reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were formed in solving problems by CBR are summarized. Moreover, the application fields of CBR are sorted. Finally, according to the idea of deep learning and interpretable artificial intelligence, the main challenges for the future development of CBR are proposed.

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Section: Other Applicationsmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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“…In this article, author used artificial intelligence to improve the results of segmentation and its implementation done on kidney and tumour images. This process complete in three layers: Fusion layer, Segmentation layer, Data layer [ 159 ]. The multi-view deep learning model is also used in Covid-19 for validation and testing sets of chest CT images.…”

Section: Image Fusion Techniquesmentioning

confidence: 99%

Image Fusion Techniques: A Survey

Kaur

Koundal

Kadyan

2021

Arch Computat Methods Eng

227

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The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

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“…Compared with other methods, CBR is more suitable for on-site optimization of emergency responses, which can provide a referential solution for decision-makers in the shortest time by analyzing prior experience. CBR has been widely applied in many domains, not only including emergency management [ 6 , 8 , 9 ] but also in complex artificial intelligence [ 10 – 13 ], waste treatment [ 14 ], and biological domain [ 15 ]. In construction projects, CBR helped a lot in risk management and estate valuation [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Research on emergency management of urban waterlogging based on similarity fusion of multi-source heterogeneous data

2022

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Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with severe economic losses and casualties. Aiming to improve the effectiveness of disaster emergency management, we propose a novel emergency decision model embedding similarity algorithms of heterogeneous multi-attribute based on case-based reasoning. First, this paper establishes a multi-dimensional attribute system of urban waterlogging catastrophes cases based on the Wuli-Shili-Renli theory. Due to the heterogeneity of attributes of waterlogging cases, different algorithms to measure the attribute similarity are designed for crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables, and hesitant fuzzy linguistic term sets. Then, this paper combines the best-worst method with the maximal deviation method for a more reasonable weight allocation of attributes. Finally, the hybrid similarity between the historical and the target cases is obtained by aggregating attribute similarities via the weighted method. According to the given threshold value, a similar historical case set is built whose emergency measures are used to provide the reference for the target case. Additionally, a case of urban waterlogging emergency is conducted to demonstrate the applicability and effectiveness of the proposed model, which exploits historical experiences and retrieves the optimal scheme for the current disaster emergency with heterogeneous multi attributes. Consequently, the proposed model solves the problem of diverse data types to satisfy the needs of case presentation and retrieval. Compared with the existing model, it can better realize the multi-dimensional expression and fast matching of the cases.

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A fusion method based on Deep Learning and Case-Based Reasoning which improves the resulting medical image segmentations

Cited by 23 publications

References 23 publications

A review of the development and future challenges of case-based reasoning

A review of the development and future challenges of case-based reasoning

Image Fusion Techniques: A Survey

Research on emergency management of urban waterlogging based on similarity fusion of multi-source heterogeneous data

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