Description |
1 online resource (xxiii, 505 pages) : illustrations (some color) |
Series |
Lecture notes in computer science, 1611-3349 ; 14366 |
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Lecture notes in computer science ; 14366. 1611-3349
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Contents |
Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Artificial Intelligence and Radiomics in Computer-Aided Diagnosis (AIRCAD) -- Leukocytes Classification Methods: Effectiveness and Robustness in a Real Application Scenario -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Sets -- 2.2 Data Pre-processing -- 2.3 Methods -- 3 Experimental Evaluation -- 3.1 Experimental Setup -- 3.2 Experimental Results -- 4 Conclusions -- References -- Vision Transformers for Breast Cancer Histology Image Classification -- 1 Introduction -- 2 Background and Related Work |
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2.1 Deep Learning in Histopathology Images of Breast Cancer -- 2.2 Vision Transformers -- 2.3 BACH: Grand Challenge on Breast Cancer Histology Images -- 3 Methodology -- 4 Experimental Evaluation -- 5 Discussion and Conclusion -- References -- Editable Stain Transformation of Histological Images Using Unpaired GANs -- 1 Introduction -- 2 Related Work -- 2.1 Overview of xAI-CycleGAN -- 2.2 SeFa Algorithm for Editable Outputs -- 2.3 cCGAN for Stain Transformation -- 3 Methods -- 3.1 Dataset -- 3.2 Separating Structure from Style -- 3.3 Editable Generation Results Using SeFa -- 4 Results |
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5 Discussion -- 6 Future Work -- References -- Assessing the Robustness and Reproducibility of CT Radiomics Features in Non-small-cell Lung Carcinoma -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Segmentation -- 2.3 Image Pre-processing and Feature Extraction -- 2.4 Statistical Analysis -- 2.5 Feature Reduction, Selection, and Machine Learning -- 3 Results -- 3.1 Statistical Analysis -- 3.2 Feature Reduction, Selection, and Machine Learning -- 4 Discussion and Conclusions -- References |
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Prediction of High Pathological Grade in Prostate Cancer Patients Undergoing [18F]-PSMA PET/CT: A Preliminary Radiomics Study -- 1 Introduction -- 2 Materials and Methods -- 2.1 PET/CT Imaging -- 2.2 Inclusion Criteria -- 2.3 The Gleason Score -- 2.4 Radiomics Analysis -- 3 Results -- 4 Discussions and Conclusion -- References -- MTANet: Multi-Type Attention Ensemble for Malaria Parasite Detection -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 YOLO Detectors and YOLOv5 -- 3.3 Convolutional Block Attention Module (CBAM) -- 3.4 Our Proposed Method: MTANet |
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3.5 Metrics -- 4 Experimental Results and Discussion -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusions -- References -- Breast Mass Detection and Classification Using Transfer Learning on OPTIMAM Dataset Through RadImageNet Weights -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Proposed Method -- 2.3 YOLO -- 3 Results -- 3.1 Breast Mass Detection -- 3.2 Breast Mass Classification -- 4 Discussion -- 5 Conclusion -- References -- Prostate Cancer Detection: Performance of Radiomics Analysis in Multiparametric MRI -- 1 Introduction -- 2 Materials and Methods |
Summary |
The two-volume set LNCS 14365 and 14366 constitutes the papers of workshops hosted by the 22nd International Conference on Image Analysis and Processing, ICIAP 2023, held in Udine, Italy, in September 2023. In total, 72 workshop papers and 10 industrial poster session papers have been accepted for publication |
Notes |
Includes author index |
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Online resource; title from PDF title page (SpringerLink, viewed January 23, 2024) |
Subject |
Image processing -- Digital techniques -- Congresses
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Image analysis -- Congresses
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Form |
Electronic book
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Author |
Foresti, Gian Luca, 1965- editor.
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Fusiello, Andrea, editor.
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Hancock, Edwin R., editor.
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ISBN |
9783031510267 |
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3031510267 |
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