LEADER 08359nam 22007215 450 001 9910855369103321 005 20240826123333.0 010 $a9783031581717 010 $a3031581717 024 7 $a10.1007/978-3-031-58171-7 035 $a(CKB)31801676300041 035 $a(MiAaPQ)EBC31308616 035 $a(Au-PeEL)EBL31308616 035 $a(DE-He213)978-3-031-58171-7 035 $a(MiAaPQ)EBC31574304 035 $a(Au-PeEL)EBL31574304 035 $a(OCoLC)1432241406 035 $a(EXLCZ)9931801676300041 100 $a20240427d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Augmentation, Labelling, and Imperfections $eThird MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings /$fedited by Yuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (178 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14379 311 08$a9783031581700 311 08$a3031581709 327 $aIntro -- 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. 327 $a3.3 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. 327 $a3 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. 330 $aThis LNCS conference volume constitutes the proceedings of the 3rd International Workshop on Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions. The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14379 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputers 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aComputing Milieux 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputers. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aComputing Milieux. 676 $a006 700 $aXue$b Yuan$01737783 701 $aChen$b Chen$0761219 701 $aChen$b Chao$0636283 701 $aZuo$b Lianrui$01737784 701 $aLiu$b Yihao$01737785 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910855369103321 996 $aData Augmentation, Labelling, and Imperfections$94159706 997 $aUNINA