A case-based training system in radiology-senology

Demigha, Souâd; Prat, Nicolas

doi:10.1109/ictta.2004.1307601

Cited by 7 publications

(9 citation statements)

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“…There are several types of approaches in interpretable machine learning, including case-based reasoning (which we use here), forcing the network to use logical conditions within its last layers [e.g., 55], or disentangling the neural network's latent space [e.g., 11]. Case-based reasoning models in medicine retrieve existing similar cases in order to determine how to handle a new case [14,34,20,28].…”

Section: Related Workmentioning

confidence: 99%

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography

Barnett,

Schwartz,

Tao

et al. 2021

Preprint

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Interpretability in machine learning models is important in high-stakes decisions, such as whether to order a biopsy based on a mammographic exam. Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present, and it can be difficult even for a radiologist to decide between watchful waiting and biopsy based on a mammogram alone. In this work, we present a framework for interpretable machine learning-based mammography. In addition to predicting whether a lesion is malignant or benign, our work aims to follow the reasoning processes of radiologists in detecting clinically relevant semantic features of each image, such as the characteristics of the mass margins. The framework includes a novel interpretable neural network algorithm that uses casebased reasoning for mammography. Our algorithm can incorporate a combination of data with whole image labelling and data with pixel-wise annotations, leading to better accuracy and interpretability even with a small number of images. Our interpretable models are able to highlight the classification-relevant parts of the image, whereas other methods highlight healthy tissue and confounding information. Our models are decision aids, rather than decision makers, aimed at better overall human-machine collaboration. We do not observe a loss in mass margin classification accuracy over a black box neural network trained on the same data.

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Section: Related Workmentioning

confidence: 99%

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography

Barnett,

Schwartz,

Tao

et al. 2021

Preprint

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“…The case-based reasoning is adopted to represent the experience of expert radiologists-senologists as cases, [15]. The case is a patient at different intervals of treatment (time).…”

Section: B the Map Process: Our Approachmentioning

confidence: 99%

A Data Warehouse System to Help Assist Breast Cancer Screening in Diagnosis, Education and Research

Demigha

2009

2009 2nd International Conference on Computer Science and Its Applications

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International audienceearly detection of breast cancer is concidered as a major public health issue. Breast cancer screening is not generalized to the entire population due to a lack of resources, staff and appropriate tools. Unfortuantely, systematic screening can result in a volulme of data which can not be managed by present computer architecture, either in terms of storage capabilities or in terms of exploitation tools. To overcome these deficiencies, we propose in this paper to disign and develop a data warehouse system in radiology-senology domain. The aim of such a system is on one hand, to support this important volume of information providing from multiple sources of data and images and for the other hand, to help assist breast cancer sereening in diagnosis, education and researc

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“…The assessment of the similarity (attributes and objects) is performed in the same manner as the global strategy and the mixed strategy. The intention 'to calculate similarities between attribute-values' of the directive DRI 3.1.2.1.1.1 allows to use the hierarchy of UML types (Demigha and Prat, 2004). Indeed, according to various types of attributes, a similarity measure is selected.…”

Section: The Elementary Strategymentioning

confidence: 99%

“…The second level (the sub-case) is one senologic episode (clinical examination, image reading, radiological interpretation, and anatomo-pathological examination) for a given patient. The third level (the sub-sub-case) represents one phase of a senologic episode for a given patient (clinical examination OR image reading…), (Demigha and Prat, 2004).…”

Section: Introductionmentioning

confidence: 99%

The Retrieval Process in the Safrs System With the Case-Based Reasoning Approach

Demigha¹

2007

Proceedings of the Ninth International Conference on Enterprise Information Systems

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The paper presents the retrieval process in the SAFRS system (system supporting the training of radiologists-senologists) with the case-based reasoning approach (CBR, which is adopted to represent the experience of expert radiologists-senologists under the form of cases) and modelized with the MAP concept. The retrieval process relies on a procedure of case-based reasoning for retrieval of similar cases formalized using a MAP, a re-use methodology named the retrieval MAP. The model of the MAP is an intentional representation system. It is based on concepts of intention and strategy. The concept of intention (or a goal) aims to capture the objective to be achieved. A strategy is the manner an intention is achieved. The retrieval process with the MAP is a multi-step/multi-algorithm process, which permits to retrieve similar cases in various modes and strategies. It is achieved according to three complex strategies: global strategy (or global retrieval strategy), elementary strategy (or elementary retrieval strategy) and mixed strategy (or mixed retrieval strategy).

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A case-based training system in radiology-senology

Cited by 7 publications

References 0 publications

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography

A Data Warehouse System to Help Assist Breast Cancer Screening in Diagnosis, Education and Research

The Retrieval Process in the Safrs System With the Case-Based Reasoning Approach

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