| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Contents |
Front Cover -- Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images -- Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images -- Copyright -- Contents -- Contributors -- Preface -- 1 -- Mammogram data analysis: Trends, challenges, and future directions -- 1. Introduction -- 1.1 Theoretical background -- 1.1.1 "Sick lobe" model -- 1.1.2 Neoductgenesis -- 1.2 Technical knowledge -- 1.3 BC diagnosis using several imaging modalities -- 1.3.1 Mammography -- 1.3.2 Ultrasound -- 1.3.3 MRI -- 1.3.4 Histopathology |
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1.3.5 Thermography -- 1.4 Risk factors -- 1.5 Advantages in mammography -- 2. Related works -- 2.1 Microcalcification detection -- 2.2 Classification of mass -- 2.3 Feature-based BC detection -- 2.4 Computer-aided mammography -- 2.5 Database for mammogram images -- 2.5.1 INbreast -- 2.5.2 CBIS-DDSM -- 2.5.3 Image retrieval in medical applications -- 2.5.4 Mammographic Image Analysis Society -- 2.5.5 Breast cancer digital repository -- 2.5.6 BancoWeb LAPIMO -- 2.5.7 UCHC DigiMammo -- 3. Current trends in mammography analysis -- 3.1 Full field digital mammography -- 3.2 Digital mammography |
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3.2.1 Computed tomography electro-optical tomographic laser mammography -- 3.3 Scintimammography -- 3.4 Optical mammography -- 3.5 Digital breast tomosynthesis -- 3.6 Future of DBT imaging -- 4. Challenges in mammogram data analysis -- 4.1 General challenges in BC measurement and analysis -- 4.1.1 Shortcomings in primary care -- 4.1.2 Public secondary healthcare clinic mammography concerns -- 4.1.3 A gap between the BC detection strategy for primary care and secondary -- 4.1.4 Potential risks of mammography -- 4.1.5 Physical and mental suffering -- 4.1.6 Biopsies |
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4.2 Obstacles to data analytics in BC -- 4.2.1 Personal encounters and obstacles to obtaining assistance -- 4.2.2 Connecting theory into practice -- 4.2.3 Carrying out mammograms -- 4.2.4 Communication -- 4.3 Breast density versus mammographic sensitivity -- 4.4 False alarms -- 4.5 Radiation dose and digital breast tomosynthesis -- 4.6 Artifacts caused by surgical staples -- 4.6.1 Imbalanced database -- 4.6.2 Insufficient standardization -- 4.7 Challenges in data analytics models -- 4.8 Robustness -- 4.9 Cyber security -- 5. Future directions of mammogram analysis -- 6. Conclusion -- References |
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2 -- AI in breast imaging: Applications, challenges, and future research -- 1. Introduction -- 1.1 Breast cancer: Statistics -- 1.2 Breast imaging techniques and common breast abnormalities -- 1.3 Mammogram datasets -- 2. Toward AI for breast cancer diagnosis -- 2.1 AI applications for mammogram-based breast cancer analysis -- 2.1.1 Breast abnormality identification and categorization -- 2.1.2 Breast mass segmentation -- 2.1.3 Breast density assessment -- 2.1.4 Breast cancer risk assessment -- 2.1.5 BI-RADS classification -- 2.1.6 Axillary node assessment -- 2.2 Challenges and future research |
| Note |
Includes index. |
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Print version record. |
| Subject |
Breast -- Cancer -- Diagnosis.
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Breast -- Cancer -- Imaging.
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Artificial intelligence -- Medical applications.
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Sein -- Cancer -- Imagerie.
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Intelligence artificielle en médecine.
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| Added Author |
Hemanth, D. Jude.
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| Other Form: |
Print version: Computational intelligence and modelling techniques for disease detection in mammogram images. Amsterdam : Academic Press, 2023 9780443139994 (OCoLC)1400077793 |
| ISBN |
9780443140006 |
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0443140006 |
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9780443139994 |
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0443139997 |
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9780443139994 |
| Standard No. |
AU@ 000076032530 |
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AU@ 000076053398 |
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