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Medical Image Learning with Limited and Noisy Data: First International Workshop, Milland 2022, Held in Conjunction with Miccai 2022, Singapore, Septe

Medical Image Learning with Limited and Noisy Data: First International Workshop, Milland 2022, Held in Conjunction with Miccai 2022, Singapore, Septe (Paperback, 2022)

Marius George Linguraru, Bagci, Ulas, Sameer Antani, Ghada Zamzmi, Sivaramakrishnan Rajaraman (엮은이)
Springer
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Medical Image Learning with Limited and Noisy Data: First International Workshop, Milland 2022, Held in Conjunction with Miccai 2022, Singapore, Septe
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책 정보

· 제목 : Medical Image Learning with Limited and Noisy Data: First International Workshop, Milland 2022, Held in Conjunction with Miccai 2022, Singapore, Septe (Paperback, 2022) 
· 분류 : 외국도서 > 컴퓨터 > 컴퓨터 비전/패턴 인식
· ISBN : 9783031167591
· 쪽수 : 240쪽
· 출판일 : 2022-09-22

목차

Efficient and Robust Annotation Strategies.- Heatmap Regression for Lesion Detection using Pointwise Annotations.-.- Partial Annotations for the Segmentation of Large Structures with Low Annotation.-.- Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment.- Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels.- Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction.- Weakly-supervised, Self-supervised, and Contrastive Learning.- Universal Lesion Detection and Classification using Limited Data and Weakly-Supervised Self-Training.- BoxShrink: From Bounding Boxes to Segmentation Masks.- Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis.- SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI.- Optimizing Transformations for Contrastive Learning in a Differentiable Framework.- Stain-based Contrastive Co-training for Histopathological Image Analysis.- Active and Continual Learning.- CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification.- Real-time Data Augmentation using Fractional Linear Transformations in Continual Learning.- DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures.- Transfer Representation Learning.- Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning.- Asymmetry and Architectural Distortion Detection with Limited Mammography Data.- Imbalanced Data and Out-of-distribution Generalization.- Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT.- CVAD: An Anomaly Detector for Medical Images Based on Cascade.- Approaches for Noisy, Missing, and Low Quality Data.- Visual Field Prediction with Missing and Noisy Data Based on Distance-based Loss.- Image Quality Classification for Automated Visual Evaluation of Cervical Precancer.- A Monotonicity Constraint Attention Module for Emotion Classification with Limited EEG Data.- Automated Skin Biopsy Analysis with Limited Data.

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