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[null,"1.","Record Nr.",null,"UNINA9910855369103321"]
[null,null,"Autore",null,"Xue Yuan"]
[null,null,"Titolo",null,"Data Augmentation, Labelling, and Imperfections : Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / edited by Yuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031581717"]
[{"$nulls":4},"3031581717"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (178 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14379"]
[null,null,"Disciplina",null,"006"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computing Milieux"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- URL: Combating Label Noise for Lung Nodule Malignancy Grading -- 1 Introduction -- 2 Method -- 2.1 Problem Definition and Overview -- 2.2 SCL Stage -- 2.3 MU Stage -- 3 Experiments and Results -- 3.1 Dataset and Experimental Setup -- 3.2 Comparative Experiments -- 3.3 Ablation Analysis -- 4 Conclusion -- References -- Zero-Shot Learning of Individualized Task Contrast Prediction from Resting-State Functional Connectomes -- 1 Introduction -- 2 Methods -- 3 Experimental Setup -- 3.1 Data -- 3.2 OPIC's Training -- 3.3 Baselines -- 3.4 Metrics -- 4 Results -- 4.1 In-Domain Prediction Quality -- 4.2 Out-of-Domain Prediction Quality -- 4.3 New Task Contrast from a Seen Task Group -- 5 Conclusion -- References -- Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Discussion -- References -- A Unified Approach to Learning with Label Noise and Unsupervised Confidence Approximation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Noisy Labels and Confidence Score Approximation -- 3.2 Unsupervised "]
[{"$nulls":4},"Confidence Approximation Loss -- 3.3 Unsupervised Confidence Approximation Architecture -- 3.4 Confidence-Selective Prediction -- 3.5 Pixelwise UCA -- 4 Experimental Results -- 5 Conclusion -- References -- Transesophageal Echocardiography Generation Using Anatomical Models -- 1 Introduction -- 2 Methods -- 2.1 Pseudo-Image Generation -- 2.2 Image Synthesis -- 3 Results and Discussion -- 4 Conclusion -- References -- Data Augmentation Based on DiscrimDiff for Histopathology Image Classification -- 1 Introduction -- 2 Method -- 2.1 Synthesizing Histopathology Images Based on Diffusion Model -- 2.2 Post-discrimination Mechanism for Diffusion -- 3 Experiments -- 3.1 Datasets and Implementation -- 3.2 Result and Discussion."]
[{"$nulls":4},"3.3 Visualization of Class-Specific Image Features -- 4 Conclusion -- References -- Clinically Focussed Evaluation of Anomaly Detection and Localisation Methods Using Inpatient CT Head Data -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Anomaly Detection Models -- 5 Clinical Evaluation Methodology -- 6 Results -- 7 Conclusion -- References -- LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation -- 1 Introduction -- 1.1 Related Works -- 1.2 Contributions -- 2 Method -- 2.1 LesionMix -- 2.2 Lesion Populating -- 2.3 Lesion Inpainting -- 2.4 Lesion Load Distribution -- 2.5 Properties of LesionMix -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Knowledge Graph Embeddings for Multi-lingual Structured Representations of Radiology Reports -- 1 Introduction -- 2 Methodology -- 3 Experimental Setup -- 4 Results and Discussion -- 5 Conclusion -- References -- Modular, Label-Efficient Dataset Generation for Instrument Detection for Robotic Scrub Nurses -- 1 Introduction -- 2 Dataset -- 2.1 Data Acquisition -- 2.2 Real Multi-instrument Data for Validation and Testing -- 2.3 Real Single-Instrument Images for Advanced MBOI -- 3 Experiments -- 3.1 Model and Hyperparameters -- 3.2 Synthetic Training Data from MBOI -- 3.3 Advancing Copy-Paste in MBOI -- 3.4 Effciency: Performance vs. Invested Resources -- 4 Results -- 4.1 Naive Insertion vs. Gaussian Blur and Poisson Blending -- 4.2 Impact of the Number of SI Images and Training Set Size -- 4.3 Evaluation of with Other Detectors Under Optimal Conditions -- 5 Conclusion -- References -- Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels -- 1 Introduction -- 2 Methodology -- 2.1 Preprocessing -- 2.2 Problem Formulation -- 2.3 Supervised Learning -- 2.4 Semi-supervised Learning."]
[{"$nulls":4},"3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Setup -- 3.3 Evaluation Metrics -- 3.4 Qualitative Results and Analysis -- 3.5 Quantitative Results and Analysis -- 4 Conclusion -- References -- Proportion Estimation by Masked Learning from Label Proportion -- 1 Introduction -- 2 PD-L1 Tumor Proportion Estimation -- 3 Experiments -- 4 Conclusion -- References -- Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset -- 1 Background -- 1.1 Active Learning -- 1.2 Active Learning Applied to Thyroid Ultrasound -- 2 Materials and Methods -- 2.1 Image Datasets -- 2.2 Rigged Draw Strategy -- 2.3 Supervised and Unsupervised Active Learning Strategies -- 3 Results -- 3.1 Supervised Strategies -- 3.2 Semi-supervised Strategies -- 4 Discussion -- References -- A Realistic Collimated X-Ray Image Simulation Pipeline -- 1 Introduction -- 2 Methods -- 2.1 Randomized Collimator Simulation Pipeline -- 2.2 Experiments -- 3 Results -- 3.1 Framework Validation -- 3.2 Network Evaluation -- 4 Discussion -- References -- Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant "]
[{"$nulls":4},"Abuse -- 1 Introduction -- 2 Methods -- 2.1 Diffusion Model -- 2.2 Image Generation via Conditional Diffusion Model -- 3 Experiments -- 4 Results and Discussion -- 5 Conclusions -- References -- Self-supervised Single-Image Deconvolution with Siamese Neural Networks -- 1 Introduction and Related Work -- 2 Methods -- 3 Experiments -- 3.1 2D Dataset -- 3.2 3D Dataset -- 4 Discussion -- 5 Conclusion -- References -- Author Index."]
[null,null,null,"Sommario/riassunto","This LNCS conference volume constitutes the proceedings of the 3rd International Workshop on Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions. The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis."]