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Liu, Y. (2024). Breast cancer risk assessment and detection in mammograms with artificial intelligence. (Doctoral dissertation). Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>Breast cancer risk assessment and detection in mammograms with artificial intelligence
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Breast cancer, the most common type of cancer among women worldwide, necessitates reliable early detection methods. Although mammography serves as a cost-effective screening technique, its limitations in sensitivity emphasize the need for more advanced detection approaches. Previous studies have relied on breast density, extracted directly from the mammograms, as a primary metric for cancer risk assessment, given its correlation with increased cancer risk and the masking potential of cancer. However, such a singular metric overlooks image details and spatial relationships critical for cancer diagnosis. To address these limitations, this thesis integrates artificial intelligence (AI) models into mammography, with the goal of enhancing both cancer detection and risk estimation. 

In this thesis, we aim to establish a new benchmark for breast cancer prediction using neural networks. Utilizing the Cohort of Screen-Aged Women (CSAW) dataset, which includes mammography images from 2008 to 2015 in Stockholm, Sweden, we develop three AI models to predict inherent risk, cancer signs, and masking potential of cancer. Combined, these models can e↵ectively identify women in need of supplemental screening, even after a clean exam, paving the way for better early detection of cancer. Individually, important progress has been made on each of these component tasks as well. The risk prediction model, developed and tested on a large population-based cohort, establishes a new state-of-the-art at identifying women at elevated risk of developing breast cancer, outperforming traditional density measures. The risk model is carefully designed to avoid conflating image patterns re- lated to early cancers signs with those related to long-term risk. We also propose a method that allows vision transformers to eciently be trained on and make use of high-resolution images, an essential property for models analyzing mammograms. We also develop an approach to predict the masking potential in a mammogram – the likelihood that a cancer may be obscured by neighboring tissue and consequently misdiagnosed. High masking potential can complicate early detection and delay timely interventions. Along with the model, we curate and release a new public dataset which can help speed up progress on this important task. 

Through our research, we demonstrate the transformative potential of AI in mammographic analysis. By capturing subtle image cues, AI models consistently exceed the traditional baselines. These advancements not only highlight both the individual and combined advantages of the models, but also signal a transition to an era of AI-enhanced personalized healthcare, promising more ecient resource allocation and better patient outcomes. 

Abstract [sv]

Bröstcancer, den vanligaste cancerformen bland kvinnor globalt, kräver tillförlitliga metoder för tidig upptäckt. Även om mammografi fungerar som en kostnadseffektiv screeningteknik, understryker dess begränsningar i känslighet behovet av mer avancerade detektionsmetoder. Tidigare studier har förlitat sig på brösttäthet, utvunnen direkt från mammogram, som en primär indikator för riskbedömning, givet dess samband med ökad cancerrisk och cancermaskeringspotential. Visserligen förbiser en sådan enskild indikator bildinformation och spatiala relationer vilka är kritiska för cancerdiagnos. För att möta dessa begränsningar integrerar denna avhandling artificiell intelligens (AI) modeller i mammografi, med målet att förbättra både cancerdetektion och riskbedömning. 

I denna avhandling syftar vi till att fastställa en ny standard för bröstcancer-prediktion med hjälp av neurala nätverk. Genom att utnyttja datasetet Co-hort of Screen-Aged Women (CSAW), som inkluderar mammografier från 2008 till 2015 i Stockholm, Sverige, utvecklar vi tre AI modeller för att förutsäga inneboende risk, tecken på cancer och cancermaskeringspotential. Sammantaget kan dessa modeller effektivt identifiera kvinnor som behöver kompletterande screening, även efter en undersökning där patienten klassificerats som hälsosam, vilket banar väg för tidigare upptäckt av cancer. Individuellt har viktiga framsteg också gjorts i vardera modell. Riskdetektionsmodellen, utvecklad och testad på en stor populationsbaserad kohort, etablerar en ny state-of-the-art vid identifiering av kvinnor med ökad risk att utveckla bröstcancer, och presterar bättre än traditionella täthetsmodeller. Riskmodellen är noggrant utformad för att undvika att sammanblanda bildmönster relaterade till tidiga tecken på cancer med de som relaterar till långsiktig risk. Vi föreslår också en metod som gör det möjligt för vision transformers att effektivt tränas på samt utnyttja högupplösta bilder, en väsentlig egenskap för modeller som berör mammogram. Vi utvecklar också en metod för att förutsäga maskeringspotentialen i mammogram - sannolikheten att en cancer kan döljas av närliggande vävnad och följaktligen misstolkas. Hög maskeringspotential kan komplicera tidig upptäckt och försena ingripanden. Tillsammans med modellen sammanställer och släpper vi ett nytt offentligt dataset som kan hjälpa till att påskynda framsteg inom detta viktiga område. 

Genom vår forskning demonstrerar vi den transformativa potentialen med AI i mammografianalys. Genom att fånga subtila bildledtrådar överträffar AI-modeller konsekvent de traditionella baslinjerna. Dessa framsteg belyser inte bara de individuella och kombinerade fördelarna med modellerna, utan signalerar också ett paradigmskifte mot en era av AI-förstärkt personlig hälso- och sjukvård, med ett löfte om mer effektiv resursallokering och förbättrade patientresultat. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2024. p. xi, 61
Series
TRITA-EECS-AVL ; 2024:2
Keywords
Mammography, AI, Breast cancer risk, Breast cancer detection, Mammografi, AI, Bröstcancerrisk, Upptäckt av bröstcancer
National Category
Engineering and Technology Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-340723 (URN)978-91-8040-783-0 (ISBN)
Public defence
2024-01-18, Air & Fire, Science for Life Laboratory, Tomtebodavägen 23A, Solna, 14:00 (English)
Opponent
Supervisors
Note

QC 20231212

Available from: 2023-12-12 Created: 2023-12-11 Last updated: 2024-01-19Bibliographically approved
Liu, Y., Sorkhei, M., Dembrower, K., Azizpour, H., Strand, F. & Smith, K. (2024). Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening. Radiology, 311(1), Article ID e232535.
Open this publication in new window or tab >>Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening
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2024 (English)In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 311, no 1, article id e232535Article in journal (Refereed) Published
Abstract [en]

Background: Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective.<br /> Purpose: To assess whether AISmartDensity-an AI -based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods: This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System "extremely dense" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test.<br /> Results: The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 ( P < .001) for AISmartDensity and the best -performing density model (age -adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best -performing density model (age -adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% ( P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next -round screen -detected cancers, whereas the best -performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively.<br /> Conclusion: AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram.

Place, publisher, year, edition, pages
Radiological Society of North America (RSNA), 2024
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-349627 (URN)10.1148/radiol.232535 (DOI)001245823000007 ()38591971 (PubMedID)2-s2.0-85190324943 (Scopus ID)
Note

QC 20240702

Available from: 2024-07-02 Created: 2024-07-02 Last updated: 2024-07-02Bibliographically approved
Liu, Y., Matsoukas, C., Strand, F., Azizpour, H. & Smith, K. (2023). PatchDropout: Economizing Vision Transformers Using Patch Dropout. In: 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV): . Paper presented at 23rd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), JAN 03-07, 2023, Waikoloa, HI (pp. 3942-3951). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>PatchDropout: Economizing Vision Transformers Using Patch Dropout
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2023 (English)In: 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 3942-3951Conference paper, Published paper (Refereed)
Abstract [en]

Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their use in many applications, especially those that depend on high-resolution images, such as medical image classification. Efforts to train ViTs more efficiently are overly complicated, necessitating architectural changes or intricate training schemes. In this work, we show that standard ViT models can be efficiently trained at high resolution by randomly dropping input image patches. This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as IMAGENET, and those savings only increase with image size. On CSAW, a high-resolution medical dataset, we observe a 5. savings in computation and memory using PatchDropout, along with a boost in performance. For practitioners with a fixed computational or memory budget, PatchDropout makes it possible to choose image resolution, hyperparameters, or model size to get the most performance out of their model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE Winter Conference on Applications of Computer Vision, ISSN 2472-6737
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-333235 (URN)10.1109/WACV56688.2023.00394 (DOI)000971500204006 ()2-s2.0-85149011721 (Scopus ID)
Conference
23rd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), JAN 03-07, 2023, Waikoloa, HI
Note

QC 20230731

Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2023-12-11Bibliographically approved
Liu, Y., Sorkhei, M., Dembrower, K., Azizpour, H., Strand, F. & Smith, K. (2023). Selecting Women for Supplemental Breast Imaging using AI Biomarkers of Cancer Signs, Masking, and Risk.
Open this publication in new window or tab >>Selecting Women for Supplemental Breast Imaging using AI Biomarkers of Cancer Signs, Masking, and Risk
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2023 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Traditional mammographic density aids in determining the need for supplemental imagingby MRI or ultrasound. However, AI image analysis, considering more subtle and complex image features,may enable a more effective identification of women requiring supplemental imaging.Purpose: To assess if AISmartDensity, an AI-based score considering cancer signs, masking, and risk,surpasses traditional mammographic density in identifying women for supplemental imaging after negativescreening mammography.Methods: This retrospective study included randomly selected breast cancer patients and healthy controlsat Karolinska University Hospital between 2008 and 2015. Bootstrapping simulated a 0.2% interval cancerrate. We included previous exams for diagnosed women and all exams for controls. AISmartDensity hadbeen developed using random mammograms from a population non-overlapping with the current studypopulation. We evaluated AISmartDensity to, based on negative screening mammograms, identify womenwith interval cancer and next-round screen-detected cancer. It was compared to age and density models, withsensitivity and PPV calculated for women with the top 8% scores, mimicking the proportion of BIRADS“extremely dense” category. Statistical significance was determined using the Student’s t-test.Results: The study involved 2043 women, 258 with breast cancer diagnosed within 3 years of a negativemammogram, and 1785 healthy controls. Diagnosed women had a median age of 57 years (IQR 16) versus53 years (IQR 15) for controls (p < .001). At the 92nd percentile, AISmartDenstiy identified 87 (33.67%)future cancers with PPV 1.68%, whereas mammographic density identified 34 (13.18%) with PPV 0.66%(p < .001). AISmartDensity identified 32% interval and 36% next-round cancers, versus mammographicdensity’s 16% and 10%. The combined mammographic density and age model yielded an AUC of 0.60,significantly lower than AISmartDensity’s 0.73 (p < .001).Conclusions: AISmartDensity, integrating cancer signs, masking, and risk, more effectively identifiedwomen for additional breast imaging than traditional age and density models. 

National Category
Medical and Health Sciences Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-340721 (URN)
Note

QC 20231218

Available from: 2023-12-11 Created: 2023-12-11 Last updated: 2023-12-18Bibliographically approved
Sorkhei, M., Liu, Y., Azizpour, H., Azavedo, E., Dembrower, K., Ntoula, D., . . . Smith, K. (2021). CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer. In: Conference on Neural Information Processing Systems (NeurIPS) – Datasets and Benchmarks Proceedings, 2021.: . Paper presented at 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 6-14 Dec 2021, virtual..
Open this publication in new window or tab >>CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
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2021 (English)In: Conference on Neural Information Processing Systems (NeurIPS) – Datasets and Benchmarks Proceedings, 2021., 2021Conference paper, Published paper (Refereed)
Abstract [en]

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers – without being explicitly trained for these tasks – than its breast density counterparts.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-340718 (URN)
Conference
35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 6-14 Dec 2021, virtual.
Note

QC 20231218

Available from: 2023-12-11 Created: 2023-12-11 Last updated: 2023-12-18Bibliographically approved
Dembrower, K., Liu, Y., Azizpour, H., Eklund, M., Smith, K., Lindholm, P. & Strand, F. (2020). Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology, 294(2), 265-272
Open this publication in new window or tab >>Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction
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2020 (English)In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 294, no 2, p. 265-272Article in journal (Refereed) Published
Abstract [en]

Background: Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose: To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods: In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results: A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P< .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P< .001), and the percentage density (mean, 25.6% vs 24.0%; P< .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P< .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P< .001); this difference was most pronounced for more aggressive cancers. Conclusion: Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.

Place, publisher, year, edition, pages
Radiological Society of North America Inc., 2020
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-267834 (URN)10.1148/radiol.2019190872 (DOI)000508455500006 ()31845842 (PubMedID)2-s2.0-85078538925 (Scopus ID)
Note

QC 20200227

Available from: 2020-02-27 Created: 2020-02-27 Last updated: 2024-03-15Bibliographically approved
Liu, Y., Azizpour, H., Strand, F. & Smith, K. (2020). Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (Lecture Notes in Computer Science). Paper presented at 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020; Lima; Peru; 4 October 2020 through 8 October 2020 (pp. 230-240). Springer Nature, 12266
Open this publication in new window or tab >>Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models
2020 (English)In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (Lecture Notes in Computer Science), Springer Nature , 2020, Vol. 12266, p. 230-240Conference paper, Published paper (Refereed)
Abstract [en]

The ability to accurately estimate risk of developing breast cancer would be invaluable for clinical decision-making. One promising new approach is to integrate image-based risk models based on deep neural networks. However, one must take care when using such models, as selection of training data influences the patterns the network will learn to identify. With this in mind, we trained networks using three different criteria to select the positive training data (i.e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis. We find that these three models learn distinctive features that focus on different patterns, which translates to contrasts in performance. Short-term risk is best estimated by the cancer signs model, whilst long-term risk is best estimated by the inherent risk model. Carelessly training with all images conflates inherent risk with early cancer signs, and yields sub-optimal estimates in both regimes. As a consequence, conflated models may lead physicians to recommend preventative action when early cancer signs are already visible.

Place, publisher, year, edition, pages
Springer Nature, 2020
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 12266
Keywords
Deep learning, Mammography, Risk prediction
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-291719 (URN)10.1007/978-3-030-59725-2_23 (DOI)2-s2.0-85092769948 (Scopus ID)
Conference
23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020; Lima; Peru; 4 October 2020 through 8 October 2020
Note

QC 20210323

Available from: 2021-03-23 Created: 2021-03-23 Last updated: 2023-12-11Bibliographically approved
Dembrower, K., Wahlin, E., Liu, Y., Salim, M., Smith, K., Lindholm, P., . . . Strand, F. (2020). Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. The Lancet Digital Health, 2(9), E468-E474
Open this publication in new window or tab >>Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
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2020 (English)In: The Lancet Digital Health, E-ISSN 2589-7500, Vol. 2, no 9, p. E468-E474Article in journal (Refereed) Published
Abstract [en]

Background We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection. Methods In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0.7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer. Findings 7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0.3% (95% CI 0.0-4.3), or 2.6% (1.1-5.4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively. Interpretation Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later.

Place, publisher, year, edition, pages
Elsevier, 2020
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-281510 (URN)10.1016/S2589-7500(20)30185-0 (DOI)000565731100011 ()33328114 (PubMedID)2-s2.0-85089681582 (Scopus ID)
Note

QC 20201019

Available from: 2020-10-19 Created: 2020-10-19 Last updated: 2022-06-25Bibliographically approved
Salim, M., Wåhlin, E., Dembrower, K., Azavedo, E., Foukakis, T., Liu, Y., . . . Strand, F. (2020). External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms. JAMA Oncology, 6(10), 1581
Open this publication in new window or tab >>External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
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2020 (English)In: JAMA Oncology, ISSN 2374-2437, E-ISSN 2374-2445, Vol. 6, no 10, p. 1581-Article in journal (Refereed) Published
Abstract [en]

Importance: A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening. Objective: To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists. Design, Setting, and Participants: This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer). Main Outcomes and Measures: Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%). Results: The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level. Conclusions and Relevance: To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers. 

Place, publisher, year, edition, pages
American Medical Association (AMA), 2020
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-284972 (URN)10.1001/jamaoncol.2020.3321 (DOI)000583209400012 ()32852536 (PubMedID)2-s2.0-85090773052 (Scopus ID)
Note

QC 20201222

Available from: 2020-12-22 Created: 2020-12-22 Last updated: 2022-06-25Bibliographically approved
Pintea, S. L., Liu, Y. & van Gemert, J. C. (2018). Recurrent knowledge distillation. In: 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP): . Paper presented at 25th IEEE International Conference on Image Processing (ICIP), OCT 07-10, 2018, Athens, GREECE (pp. 3393-3397). IEEE
Open this publication in new window or tab >>Recurrent knowledge distillation
2018 (English)In: 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), IEEE , 2018, p. 3393-3397Conference paper, Published paper (Refereed)
Abstract [en]

Knowledge distillation compacts deep networks by letting a small student network learn from a large teacher network. The accuracy of knowledge distillation recently benefited from adding residual layers. We propose to reduce the size of the student network even further by recasting multiple residual layers in the teacher network into a single recurrent student layer. We propose three variants of adding recurrent connections into the student network, and show experimentally on CIFAR-10, Scenes and MiniPlaces, that we can reduce the number of parameters at little loss in accuracy.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Image Processing ICIP, ISSN 1522-4880
Keywords
Knowledge distillation, compacting deep representations for image classification, recurrent layers, NG J, 2009, PROC CVPR IEEE, P248 J, 2014, NIPS, P2654 X, 2017, CVPR, P7370 ringenberg J T, 2013, CoRR, ng Yunhe, 2017, ICML, P3703 izhevsky Alex, 2009, Learning multiple layers of features from tiny images, n M, 2013, ICLR, m J, 2017, CVPR, P4133 goruyko Sergey, 2017, ICLR, eff K, 2016, ICLR, odfellow I J, 2013, JMLR, elhamer Evan, 2017, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, V39, P640 bedev Vadim, 2016, 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), JUN 27-30, 2016, Seattle, WA, P2554 pta Saurabh, 2016, 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), JUN 27-30, 2016, Seattle, WA, P2827 stegari Mohammad, 2016, COMPUTER VISION - ECCV 2016, PT IV14th European Conference on Computer Vision (ECCV), OCT 08-16, 2016, Amsterdam, NETHERLANDS, V9908, P525 ang Xiangyu, 2016, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, V38, P1943 Kaiming, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)IEEE International Conference on Computer Vision, DEC 11-18, 2015, Santiago, CHILE, P1026 eng Yu, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)IEEE International Conference on Computer Vision, DEC 11-18, 2015, Santiago, CHILE, P2857 ang Ming, 2015, 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)IEEE Conference on Computer Vision and Pattern Recognition (CVPR), JUN 07-12, 2015, Boston, MA, P3367 chreiter S, 1997, NEURAL COMPUTATION, V9, P1735 attoni Ariadna, 2009, CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops, JUN 20-25, 2009, Miami Beach, FL, P413 nton G, 2015, NIPS Deep Learning and Representation Learning Workshop, i J, 2016, NIPS, P379
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-242213 (URN)10.1109/ICIP.2018.8451253 (DOI)000455181503102 ()2-s2.0-85057102091 (Scopus ID)978-1-4799-7061-2 (ISBN)
Conference
25th IEEE International Conference on Image Processing (ICIP), OCT 07-10, 2018, Athens, GREECE
Note

QC 20190128

Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2024-03-15Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0101-1505

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