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References from An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/j.inffus.2019.12.012
10.1016/j.inffus.2019.12.012 · External reference
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A case-based interpretable deep learning model for classification of mass lesions in digital mammography
10.1038/s42256-021-00423-x · 2021 · External reference
10.1109/isbi.2018.8363762
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A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images
10.1001/jamanetworkopen.2021.19100 · 2021 · External reference
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2022 · External reference
This looks like that: deep learning for interpretable image recognition
2019 · External reference
Digital breast tomosynthesis: concepts and clinical practice
10.1148/radiol.2019180760 · 2019 · External reference
Deep learning for image and point cloud fusion in autonomous driving: a review
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Object detection using yolo: challenges, architectural successors, datasets and applications
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Trends in breast cancer mortality by race/ethnicity, age, and us census region, United States (1999-2020)
10.1002/cncr.34503 · 2023 · External reference
Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network
10.1016/j.ymeth.2019.02.010 · 2019 · External reference
Developing breast lesion detection algorithms for digital breast tomosynthesis: leveraging false positive findings
10.1002/mp.15883 · 2022 · External reference
Unresolved reference
External reference
Unresolved reference
2022 · External reference
A competition, benchmark, code, and data for using artificial intelligence to detect lesions in digital breast tomosynthesis
10.1001/jamanetworkopen.2023.0524 · 2023 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
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10.1145/2939672.2939778
10.1145/2939672.2939778 · 2016 · External reference
10.1038/s42256-019-0048-x
10.1038/s42256-019-0048-x · External reference
10.1109/iccv.2017.74
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An interpretable deep learning model for covid-19 detection with chest x-ray images
10.1109/access.2021.3087583 · 2021 · External reference
Object or background: an interpretable deep learning model for covid-19 detection from ct-scan images
10.3390/diagnostics11091732 · 2021 · External reference
These do not look like those: an interpretable deep learning model for image recognition
10.1109/access.2021.3064838 · 2021 · External reference
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Incidence, mortality, survival, and disease burden of breast cancer in China compared to other developed countries
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2021 · External reference
Breast cancer mortality in 500 000 women with early invasive breast cancer in England, 1993-2015: population based observational cohort study
10.1136/bmj-2022-074684 · 2023 · External reference
10.1016/s0140-6736(22)01438-6
10.1016/s0140-6736(22)01438-6 · External reference
An interpretable and accurate deep-learning diagnosis framework modelled with fully and semi-supervised reciprocal learning
2023 · External reference
Review on deep learning techniques for marine object recognition: architectures and algorithms
10.1016/j.conengprac.2020.104458 · 2022 · External reference
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External reference
Global trends and forecasts of breast cancer incidence and deaths
10.1038/s41597-023-02253-5 · 2023 · External reference
10.1109/bibm47256.2019.8983097
10.1109/bibm47256.2019.8983097 · 2019 · External reference
