Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
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
| 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 |
| Autore | Xue Yuan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (178 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
ChenChen
ChenChao ZuoLianrui LiuYihao |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Computers Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computing Milieux |
| ISBN |
9783031581717
3031581717 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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.
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. 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 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. |
| Record Nr. | UNINA-9910855369103321 |
Xue Yuan
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Simulation and Synthesis in Medical Imaging : 10th International Workshop, SASHIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings
| Simulation and Synthesis in Medical Imaging : 10th International Workshop, SASHIMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings |
| Autore | Fernandez Virginia |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (311 pages) |
| Disciplina | 616.0754 |
| Altri autori (Persone) |
WiesnerDavid
ZuoLianrui CasamitjanaAdrià RemediosSamuel W |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 9783032055736 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996678678003316 |
|
Fernandez Virginia
|
||
| Cham : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||