Classification of mammographic microcalcification clusters with machine learning confidence levels

Rampun, Andrik; Wang, Hui; Scotney, Bryan; Morrow, Philip; Zwiggelaar, Reyer

doi:10.1117/12.2318058

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…This is similar to other tasks such as microcalcification or lesion detection as they dont usually appear close to the pectoral muscle boundary (Rampun et al, 2018c).…”

Section: Up-sampling Resulting Contoursupporting

confidence: 71%

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

Rampun

López-Linares

Morrow

et al. 2019

Medical Image Analysis

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This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on handcrafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find 'contour-like' objects in mammograms. The proposed framework produced

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“…This is similar to other tasks such as microcalcification or lesion detection as they dont usually appear close to the pectoral muscle boundary (Rampun et al, 2018c).…”

Section: Up-sampling Resulting Contoursupporting

confidence: 71%

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

Rampun

López-Linares

Morrow

et al. 2019

Medical Image Analysis

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“…This study could lead to a development of a new evaluation metric which could be used to estimate the degree of certainty of the system, hence provides positive potential impacts of CAD systems [8,17]. This study indicates that using A and AU C in conjunction with confidence measure to provide a more transparent representation of the actual reliability of CAD systems which is similar to our previous study [18]. In conclusion, we have studied the reliability of the confidence measures of 11 different classifiers for the breast mass classification which indicates that although a system could produce high accuracy or AU C, it does not provide a full indication about the confidence reliability of the system.…”

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confidence: 80%

Confidence Analysis for Breast Mass Image Classification

Rampun

Wang

Scotney

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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Computer-aided diagnosis (CAD) has great potential in providing real benefits to doctors and patients. Recent studies have, however, found lack of trust in CAD by radiologists in clinical diagnostic decision making. One of the main reasons is the lack of an appropriate confidence measure. This paper presents the first-ever study of classification confidence in the context of breast mass classification. We evaluated 11 state-of-the-art classification algorithms on breast mass image data using their confidence of classification metric, in addition to other standard evaluation metrics including accuracy and area under the curve (ROC). Experimental results show that although most classifiers produced very similar results with less than 2% difference in terms of accuracy and ROC, their performances are significantly different in terms of confidence levels. We suggest that the confidence measure should be used in conjunction with the existing performance metrics such as accuracy and ROC.

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“…Exploratory studies for breast mass classification [ 153 ] and classification of microcalcifications in mammograms [ 154 ] using data sets taken from the Curated Breast Imaging Subset of DDSM [ 155 ] showed that although most classifiers produce similar overall ACC and AUC values, their performances differ significantly in terms of confidence measure. High ACC or AUC does not provide a full indication of the confidence level of a CAD system.…”

Section: Resultsmentioning

confidence: 99%

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

Kondylakis¹,

Axenie²,

Bastola³

et al. 2020

J Med Internet Res

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Background The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)–funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020–funded projects. Methods Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

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Classification of mammographic microcalcification clusters with machine learning confidence levels

Cited by 12 publications

References 22 publications

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

Confidence Analysis for Breast Mass Image Classification

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

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