Domain adaptation and representation...
DART (Workshop) (2019 :)

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  • Domain adaptation and representation transfer and medical image learning with less labels and imperfect data = first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019 : proceedings /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Domain adaptation and representation transfer and medical image learning with less labels and imperfect data/ edited by Qian Wang ... [et al.].
    其他題名: first MICCAI Workshop, DART 2019, and first International Workshop, MIL3ID 2019, Shenzhen, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019 : proceedings /
    其他題名: DART 2019
    其他作者: Wang, Qian.
    團體作者: DART (Workshop)
    出版者: Cham :Springer International Publishing : : 2019.,
    面頁冊數: xvii, 254 p. :ill. (some col.), digital ;24 cm.
    內容註: DART 2019 -- Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation -- Temporal Consistency Objectives Regularize the Learning of Disentangled Representations -- Multi-layer Domain Adaptation for Deep Convolutional Networks -- Intramodality Domain Adaptation using Self Ensembling and Adversarial Training -- Learning Interpretable Disentangled Representations using Adversarial VAEs -- Synthesising Images and Labels Between MR Sequence Types With CycleGAN -- Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning -- Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans -- A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection -- Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images -- Improving Pathological Structure Segmentation Via Transfer Learning Across Diseases -- Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions -- MIL3ID 2019 -- Self-supervised learning of inverse problem solvers in medical imaging -- Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation -- A Cascade Attention Network for Liver Lesion Classification in Weakly-labeled Multi-phase CT Images -- CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT -- Active Learning Technique for Multimodal Brain Tumor Segmentation using Limited Labeled Images -- Semi-supervised Learning of Fetal Anatomy from Ultrasound -- Multi-modal segmentation with missing MR sequences using pre-trained fusion networks -- More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation -- Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition -- A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation -- Transfer Learning from Partial Annotations for Whole Brain Segmentation -- Learning to Segment Skin Lesions from Noisy Annotations -- A Weakly Supervised Method for Instance Segmentation of Biological Cells -- Towards Practical Unsupervised Anomaly Detection on Retinal Images -- Fine tuning U-Net for ultrasound image segmentation: which layers -- Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance.
    Contained By: Springer eBooks
    標題: Diagnostic imaging - Congresses. - Data processing -
    電子資源: https://doi.org/10.1007/978-3-030-33391-1
    ISBN: 9783030333911
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W9393669 電子資源 11.線上閱覽_V 電子書 EB RC78.7.D53 D37 2019 一般使用(Normal) 在架 0
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