Medical image learning with limited and noisy data : first international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings / / edited by Ghada Zamzmi [and five others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (243 pages)
• Disciplina: 733
• Soggetto topico: Deep learning (Machine learning)
• Persona (resp. second.): ZamzmiGhada
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Efficient and Robust Annotation Strategies -- Heatmap Regression for Lesion Detection Using Pointwise Annotations -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Training via Heatmap Regression -- 3.2 Detection During Inference -- 3.3 Segmentation Transfer Learning -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Lesion Detection Results -- 4.3 Lesion Segmentation via Transfer Learning -- 5 Discussion and Conclusion -- References -- Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost -- 1 Introduction -- 2 Method -- 2.1 Selective Dice Loss -- 2.2 Optimization -- 3 Experimental Results -- 4 Conclusion -- References -- Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment -- 1 Introduction -- 2 Materials and Method -- 2.1 Data -- 2.2 Architecture -- 2.3 Multiple Instance Learning for Cancer Grade Assessment -- 2.4 Noisy Labels and Weak Supervision -- 3 Experiments -- 3.1 Implementation and Evaluation -- 3.2 Results -- 4 Conclusion -- References -- Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels -- 1 Introduction -- 2 Methodology -- 2.1 Meta Pixel Loss Correction -- 2.2 Optimization Algorithm -- 3 Experiment Results -- 3.1 Dataset -- 3.2 Experiment Setting -- 3.3 Experimental Results -- 3.4 Limitation -- 4 Conclusion -- References -- Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction -- 1 Introduction -- 2 Materials -- 3 Study Design -- 4 Methods -- 4.1 Label Induction Using Machine Annotator -- 4.2 Similar Nodule Retrieval Using Metric Learning -- 5 Experiments and Results -- 5.1 Implementation -- 5.2 Quantitative Evaluation -- 5.3 Discussion -- 6 Conclusion and Future Work -- References. ; Weakly-Supervised, Self-supervised, and Contrastive Learning -- Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- BoxShrink: From Bounding Boxes to Segmentation Masks -- 1 Introduction -- 2 Related Work -- 3 Boxshrink Framework -- 3.1 Main Components -- 3.2 rapid-BoxShrink -- 3.3 robust-BoxShrink -- 4 Experiments -- 4.1 Qualitative and Quantitative Experiments -- 4.2 Reproducibility Details -- 5 Discussion -- 6 Conclusion -- References -- Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Conclusion -- References -- SB-SSL: Slice-Based Self-supervised Transformers for Knee Abnormality Classification from MRI -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Vision Transformer -- 3.2 Self-supervised Pretraining -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- Optimizing Transformations for Contrastive Learning in a Differentiable Framework -- 1 Introduction -- 2 Transformation Network -- 2.1 Optimizing Transformations -- 2.2 Differentiable Formulation of the Transformations -- 2.3 Experimental Settings -- 2.4 Linear Evaluation -- 3 Results and Discussion -- 4 Conclusions and Perspectives -- References -- Stain Based Contrastive Co-training for Histopathological Image Analysis -- 1 Introduction -- 2 Stain Based Contrastive Co-training -- 2.1 Stain Separation -- 2.2 Contrastive Co-training -- 3 Experiments -- 3.1 Datasets -- 3.2 Model Selection, Training and Hyperparameters -- 3.3 Results -- 3.4 Co-training View Analysis -- 3.5 Ablation Studies -- 4 Conclusion -- References. ; Active and Continual Learning -- .26em plus .1em minus .1emCLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contributions -- 2 Preliminaries -- 2.1 Examples of Smi Functions -- 3 Clinical: Our Targeted Active Learning Framework for Binary and Long-Tail Imbalance -- 4 Experiments -- 4.1 Binary Imbalance -- 4.2 Long-Tail Imbalance -- 5 Conclusion -- References -- Real Time Data Augmentation Using Fractional Linear Transformations in Continual Learning -- 1 Introduction -- 2 Methodology -- 3 Experiments, Results and Discussion -- 4 Conclusion -- References -- DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures -- 1 Introduction -- 1.1 Problem Statement: OOD Scenarios in Medical Data -- 1.2 Related Work -- 1.3 Our Contributions -- 2 Preliminaries -- 3 Leveraging Submodular Information Measures for Multiple Out-of-Distribution Scenarios -- 4 Experimental Results -- 4.1 Scenario A - Unrelated Images -- 4.2 Scenario B - Incorrectly Acquired Images -- 4.3 Scenario C - Mixed View Images -- 5 Conclusion -- References -- Transfer Representation Learning -- Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning -- 1 Introduction -- 2 Method -- 3 Data and Experiments -- 4 Results -- 4.1 Numerical Validation and Ablation Study -- 5 Conclusion -- References -- Asymmetry and Architectural Distortion Detection with Limited Mammography Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiment Design -- 5 Experimental Results -- 5.1 Comparison with Other Methods -- 5.2 Ablation Study -- 6 Conclusions -- References -- Imbalanced Data and Out-of-Distribution Generalization -- Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT -- 1 Introduction -- 2 Methods -- 3 Experiments. ; 4 Results and Discussion -- 5 Conclusion -- References -- CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE -- 1 Introduction -- 2 Method -- 2.1 CVAD Architecture -- 2.2 Combined Loss Function -- 2.3 Network Details -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- Approaches for Noisy, Missing, and Low Quality Data -- Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss -- 1 Introduction -- 2 Method -- 2.1 Distance-Based Loss -- 3 Experiments -- 3.1 Dataset and Implementation -- 3.2 Results -- 4 Conclusion -- References -- Image Quality Classification for Automated Visual Evaluation of Cervical Precancer -- 1 Introduction -- 2 Image Quality Labeling Criteria and Data -- 2.1 The Labeling Criteria -- 2.2 Datasets -- 3 Methods -- 3.1 Cervix Detection -- 3.2 Quality Classification -- 3.3 Mislabel Identification -- 4 Experimental Results and Discussion -- 5 Conclusions -- Appendix -- References -- A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments -- 4.1 Models' Scalp Attention Pattern -- 4.2 Models' Sensitivity of Prediction on Inputs' Frequency -- 4.3 Model Sensitivity on Morphisms Between Samples -- 5 Conclusion -- References -- Automated Skin Biopsy Analysis with Limited Data -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Nerve Labeling -- 2.3 Dermis-Epidermis Boundary Detection -- 2.4 Nerve Crossing Identification -- 3 Experimental Setup -- 3.1 Evaluating the Nerve Tracing Model -- 3.2 Evaluating Dermis Model -- 4 Results -- 4.1 Nerve Labeling Results -- 4.2 Dermis Labeling Results -- 4.3 Crossing Count Results -- 5 Discussion -- References -- Author Index.
• Titolo autorizzato: Medical image learning with limited and noisy data
• ISBN: 3-031-16760-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996490353303316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science Ser.