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Subject : breast cancer diagnosis

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Academic Journal

A Low-Cost Optomechatronic Diffuse Optical Mammography System for 3D Image Reconstruction: Proof of Concept.

Authors : Rivera-Fernández, Josué D.¹ (AUTHOR); Hernández-Mendoza, Alfredo^1,2 (AUTHOR); Fabila-Bustos, Diego A.^1,3 (AUTHOR)

Source: Diagnostics (2075-4418). Mar2025, Vol. 15 Issue 5, p584. 22p.

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Academic Journal

Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis.

Authors : Neeter, Lidewij M. F. H.^1,2 ; Robbe, M. M. Quirien²; van Nijnatten, Thiemo J. A.^1,2

Source: Journal of Cancer. 2023, Vol. 14 Issue 1, p174-182. 9p.

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Academic Journal

The added value of digital breast tomosynthesis over digital mammography in evaluation of suspicious breast lesions.

Authors : Abdullah, Riham A. S.¹ ; Oun, Abdel A. K.¹; Ahmed, Abdullah H.¹

Source: Scientific Journal of Al-Azhar Medical Faculty, Girls. Jan-Mar2021, Vol. 5 Issue 1, p67-72. 6p.

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Academic Journal

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis.

Authors : Rivera-Fernández, Josué D.¹ (AUTHOR) ; Roa-Tort, Karen¹ (AUTHOR); Stolik, Suren² (AUTHOR)

Source: Sensors (14248220). May2023, Vol. 23 Issue 9, p4390. 18p.

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Academic Journal

Role of high resolution ultrasound complementary to digital mammography.

Authors : Gupta, Kamini¹; Sandhu, Parambir¹; Arora, Srishti¹

Source: Annals of African Medicine. Jul-Sep2018, Vol. 17 Issue 3, p117-124. 8p. 6 Black and White Photographs, 10 Diagrams, 2 Charts.

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Academic Journal

Surveillance Mammography Behaviors in Black and White Breast Cancer Survivors: Behavioral Risk Factors and Surveillance System, 2016.

Authors : Edmonds, Megan C.¹ ; Dahman, Bassam²; Kim, Sunny Jung^2,3

Source: BMC Women's Health. 3/30/2023, Vol. 23 Issue 1, p1-9. 9p.

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Academic Journal

CADNet157 model: fine-tuned ResNet152 model for breast cancer diagnosis from mammography images.

Authors : Mokni, Raouia^1,2 (AUTHOR) ; Haoues, Mariem^1,3 (AUTHOR)

Source: Neural Computing & Applications. Dec2022, Vol. 34 Issue 24, p22023-22046. 24p.

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Academic Journal

Influence of Discomfort Tolerance of Women who Undergo Mammography on the Perceived Pain Intensity Due to the Procedure.

Authors : Akansel, Neriman¹ ; Gülşen, Muaz²; Gültaş, Muhammed¹

Source: European Journal of Breast Health. Jan2021, Vol. 17 Issue 1, p68-75. 8p.

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Academic Journal

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

Authors : Tamez-Peña, Jose-Gerardo¹; Rodriguez-Rojas, Juan-Andrés¹; Gomez-Rueda, Hugo¹

Source: PLoS ONE. 3/29/2018, Vol. 13 Issue 3, p1-20. 20p.

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Academic Journal

Learning multi-frequency features in convolutional network for mammography classification.

Authors : Wang, Yiming¹ (AUTHOR); Qi, Yunliang¹ (AUTHOR); Xu, Chunbo¹ (AUTHOR)

Source: Medical & Biological Engineering & Computing. Jul2022, Vol. 60 Issue 7, p2051-2062. 12p. 3 Color Photographs, 4 Diagrams, 3 Charts, 4 Graphs.

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فلترة النتائج

A Low-Cost Optomechatronic Diffuse Optical Mammography System for 3D Image Reconstruction: Proof of Concept.

Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis.

The added value of digital breast tomosynthesis over digital mammography in evaluation of suspicious breast lesions.

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis.

Role of high resolution ultrasound complementary to digital mammography.

Surveillance Mammography Behaviors in Black and White Breast Cancer Survivors: Behavioral Risk Factors and Surveillance System, 2016.

CADNet157 model: fine-tuned ResNet152 model for breast cancer diagnosis from mammography images.

Influence of Discomfort Tolerance of Women who Undergo Mammography on the Perceived Pain Intensity Due to the Procedure.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

Learning multi-frequency features in convolutional network for mammography classification.

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