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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Keywords  |
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Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
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Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Amer Medical Assoc |
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English |
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ISSN |
2574-3805 |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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Author |
Tortajada, S.; Albiol, F.; Caballero, L.; Albiol, A.; Leganes-Nieto, J.L. |
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Title |
A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization |
Type |
Journal Article |
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Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
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Volume |
13 |
Issue |
1 |
Pages |
12284 - 10pp |
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Abstract |
One of the main activities of the nuclear industry is the characterisation of radioactive waste based on the detection of gamma radiation. Large volumes of radioactive waste are classified according to their average activity, but often the radioactivity exceeds the maximum allowed by regulators in specific parts of the bulk. In addition, the detection of the radiation is currently based on static detection systems where the geometry of the bulk is fixed and well known. Furthermore, these systems are not portable and depend on the transport of waste to the places where the detection systems are located. However, there are situations where the geometry varies and where moving waste is complex. This is especially true in compromised situations.We present a new model for nuclear waste management based on a portable and geometry-independent tomographic system for three-dimensional image reconstruction for gamma radiation detection. The system relies on a combination of a gamma radiation camera and a visible camera that allows to visualise radioactivity using augmented reality and artificial computer vision techniques. This novel tomographic system has the potential to be a disruptive innovation in the nuclear industry for nuclear waste management. |
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Address |
[Tortajada, Salvador; Albiol, Francisco; Caballero, Luis] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46980 Paterna Valencia, Spain, Email: s.tortajada@ific.uv.es |
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Publisher |
Nature Portfolio |
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English |
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Edition |
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ISSN |
2045-2322 |
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Notes |
WOS:001041587900052 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5612 |
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Author |
Albiol, F.; Corbi, A.; Albiol, A. |
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Title |
Geometrical Calibration of X-Ray Imaging With RGB Cameras for 3D Reconstruction |
Type |
Journal Article |
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Year |
2016 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
IEEE Trans. Med. Imaging |
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Volume |
35 |
Issue |
8 |
Pages |
1952-1961 |
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Keywords |
3D reconstruction; camera system; geometric calibration; visible fiducials; X-ray imaging |
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Abstract |
We present a methodology to recover the geometrical calibration of conventional X-ray settings with the help of an ordinary video camera and visible fiducials that are present in the scene. After calibration, equivalent points of interest can be easily identifiable with the help of the epipolar geometry. The same procedure also allows the measurement of real anatomic lengths and angles and obtains accurate 3D locations from image points. Our approach completely eliminates the need for X-ray-opaque reference marks (and necessary supporting frames) which can sometimes be invasive for the patient, occlude the radiographic picture, and end up projected outside the imaging sensor area in oblique protocols. Two possible frameworks are envisioned: a spatially shifting X-ray anode around the patient/object and a moving patient that moves/rotates while the imaging system remains fixed. As a proof of concept, experiences with a device under test (DUT), an anthropomorphic phantom and a real brachytherapy session have been carried out. The results show that it is possible to identify common points with a proper level of accuracy and retrieve three-dimensional locations, lengths and shapes with a millimetric level of precision. The presented approach is simple and compatible with both current and legacy widespread diagnostic X-ray imaging deployments and it can represent a good and inexpensive alternative to other radiological modalities like CT. |
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Address |
[Albiol, Francisco; Corbi, Alberto] Univ Valencia, Consejo Super Invest Cient, Inst Fis Corpuscular IFIC, Paterna 46980, Spain, Email: kiko@ific.uv.es; |
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Ieee-Inst Electrical Electronics Engineers Inc |
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English |
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Original Title |
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ISSN |
0278-0062 |
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Notes |
WOS:000381436000016 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
2781 |
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Author |
Agramunt, J. et al; Tain, J.L.; Albiol, F.; Algora, A.; Domingo-Pardo, C.; Jordan, M. D.; Rubio, B.; Tarifeño-Saldivia, A.; Valencia, E. |
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Title |
Characterization of a neutron-beta counting system with beta-delayed neutron emitters |
Type |
Journal Article |
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Year |
2016 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
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Volume |
807 |
Issue |
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Pages |
69-78 |
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Keywords |
Beta-delayed neutron emission probability; Neutron and beta counters; Self-triggered digital data acquisition system; Geant4 simulations |
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Abstract |
A new detection system for the measurement of beta-delayed neutron emission probabilities has been characterized using fission products with well known beta-delayed neutron emission properties. The setup consists of BELEN-20, a 4 pi-neutron counter with twenty He-3 proportional tubes arranged inside a large polyethylene neutron moderator, a thin Si detector for beta counting and a self-triggering digital data acquisition system. The use of delayed-neutron precursors with different neutron emission windows allowed the study of the effect of energy dependency on neutron, beta and beta-neutron rates. The observed effect is well reproduced by Monte Carlo simulations. The impact of this dependency on the accuracy of neutron emission probabilities is discussed. A new accurate value of the neutron emission probability for the important delayed-neutron precursor I-137 was obtained, P-n = 7.76(14)%. |
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Address |
[Agramunt, J.; Tain, J. L.; Albiol, E.; Algora, A.; Domingo-Pardo, C.; Jordan, M. D.; Rubio, B.; Tarifeno-Saldivia, A.; Valencia, E.] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46071 Valencia, Spain, Email: Tain@ific.uv.es |
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Publisher |
Elsevier Science Bv |
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English |
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ISSN |
0168-9002 |
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Notes |
WOS:000365596200010 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2481 |
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Author |
Albiol, A.; Corbi, A.; Albiol, F. |
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Title |
Automatic intensity windowing of mammographic images based on a perceptual metric |
Type |
Journal Article |
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Year |
2017 |
Publication |
Medical Physics |
Abbreviated Journal |
Med. Phys. |
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Volume |
44 |
Issue |
4 |
Pages |
1369-1378 |
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Keywords |
contrast stretching; Gabor filtering; human visual system; mammogram; mutual information; window level/width |
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Abstract |
Purpose: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. Methods: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at . Results: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. Conclusions: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram. |
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Address |
[Albiol, Alberto] Univ Politecn Valencia, iTeam Res Inst, Valencia, Spain, Email: alberto.corbi@ific.uv.es |
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Publisher |
Wiley |
Place of Publication |
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English |
Summary Language |
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ISSN |
0094-2405 |
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Notes |
WOS:000400572700016 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
3122 |
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