Titlebook: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf; First MICCAI Worksho Qian Wang,Fausto

职业

GRAVE

Temporal Consistency Objectives Regularize the Learning of Disentangled Representationsrove semi-supervised segmentation, especially when very few labelled data are available. Specifically, we show Dice increase of up to 19% and 7% compared to supervised and semi-supervised approaches respectively on the ACDC dataset. Code is available at: ..

optic-nerve

引导

nonplus

气候

Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site T mechanism and dropout, while it does not increase parameters and computational costs, making it well-suited for small neuroimaging datasets. We evaluated our method on a challenging Traumatic Brain Injury (TBI) dataset collected from 13 sites, using labeled source data of only 14 . subjects. Experi

现存

Improving Pathological Structure Segmentation via Transfer Learning Across Diseasesi-modal MRI samples with expert-derived lesion labels. We explore several transfer learning approaches to leverage the learned MS model for the task of multi-class brain tumor segmentation on the BraTS 2018 dataset. Our results indicate that adapting and fine-tuning the encoder and decoder of the ne

Antagonism

Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performancons. We also compared the localization and classification performance with and without image augmentation by using generated VIC images. Our results show that the model trained on IC and VIC images had the highest performance in both localization and classification. Therefore, VIC images are useful

保守

Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-Propagationhe public lumbar CT dataset. On the first dataset, WISS achieves distinct improvements with regard to two different backbones. For the second dataset, WISS achieves dice coefficients of . and . for mid-sagittal slices and 3D CT volumes, respectively, saving a lot of labeling costs and only sacrifici

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