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001449868 001__ 1449868
001449868 003__ OCoLC
001449868 005__ 20230310004422.0
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001449868 019__ $$a1345582093
001449868 020__ $$a9783031167607$$q(electronic bk.)
001449868 020__ $$a3031167600$$q(electronic bk.)
001449868 020__ $$z9783031167591
001449868 020__ $$z3031167597
001449868 0247_ $$a10.1007/978-3-031-16760-7$$2doi
001449868 035__ $$aSP(OCoLC)1346149387
001449868 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ$$dUKAHL
001449868 049__ $$aISEA
001449868 050_4 $$aR857.O6
001449868 08204 $$a616.07/54$$223/eng/20220928
001449868 1112_ $$aMILLanD (Workshop)$$n(1st :$$d2022 :$$cSingapore).
001449868 24510 $$aMedical image learning with limited and noisy data :$$bfirst international workshop, MILLanD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings /$$cGhada Zamzmi, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman, Zhiyun Xue (eds.).
001449868 24630 $$aMILLanD 2022
001449868 264_1 $$aCham :$$bSpringer,$$c[2022]
001449868 264_4 $$c©2022
001449868 300__ $$a1 online resource (xi, 240 pages) :$$billustrations (chiefly color).
001449868 336__ $$atext$$btxt$$2rdacontent
001449868 337__ $$acomputer$$bc$$2rdamedia
001449868 338__ $$aonline resource$$bcr$$2rdacarrier
001449868 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13559
001449868 500__ $$aInternational conference proceedings.
001449868 500__ $$aIncludes author index.
001449868 5050_ $$aEfficient 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.
001449868 506__ $$aAccess limited to authorized users.
001449868 520__ $$aThis book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
001449868 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 28, 2022).
001449868 650_0 $$aImaging systems in medicine$$vCongresses.
001449868 650_0 $$aDeep learning (Machine learning)$$vCongresses.
001449868 655_0 $$aElectronic books.
001449868 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001449868 655_7 $$aConference papers and proceedings.$$2lcgft
001449868 7001_ $$aZamzmi, Ghada,$$eeditor.
001449868 7001_ $$aAntani, Sameer K.,$$eeditor.
001449868 7001_ $$aBagci, Ulas,$$eeditor.
001449868 7001_ $$aLinguraru, Marius George,$$eeditor.
001449868 7001_ $$aRajaraman, Sivaramakrishnan,$$eeditor.
001449868 7001_ $$aXue, Zhiyun,$$eeditor.
001449868 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(25th :$$d2022 :$$cSingapore)
001449868 77608 $$iPrint version: $$z3031167597$$z9783031167591$$w(OCoLC)1340646020
001449868 830_0 $$aLecture notes in computer science ;$$v13559.$$x1611-3349
001449868 852__ $$bebk
001449868 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-16760-7$$zOnline Access$$91397441.1
001449868 909CO $$ooai:library.usi.edu:1449868$$pGLOBAL_SET
001449868 980__ $$aBIB
001449868 980__ $$aEBOOK
001449868 982__ $$aEbook
001449868 983__ $$aOnline
001449868 994__ $$a92$$bISE