Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

amorphous

M-GenSeg: Domain Adaptation for?Target Modality Tumor Segmentation with?Annotation-Efficient Superviy. We evaluated the performance on a brain tumor segmentation dataset composed of four different contrast sequences from the public BraTS 2020 challenge data. We report consistent improvement in Dice scores over state-of-the-art domain-adaptive baselines on the unannotated target modality. Unlike th

Aggrandize

Factor Space and?Spectrum for?Medical Hyperspectral Image Segmentationperiments show our strategy leads to remarkable performance gains in different 2D architectures, reporting an improvement up to . compared with its 2D counterpart in terms of DSC on a public Multi-Dimensional Choledoch dataset. Code is publicly available at ..

遺棄

彎彎曲曲

QCResUNet: Joint Subject-Level and?Voxel-Level Prediction of?Segmentation Quality tumor segmentation QC. To train the proposed model, we created a wide variety of segmentation-quality results by using i) models that have been trained for a varying number of epochs with different modalities; and ii) a newly devised segmentation-generation method called SegGen. The proposed method

浸軟

Consistency-Guided Meta-learning for?Bootstrapping Semi-supervised Medical Image Segmentationloser to those of clean data, which is based on a small set of precisely annotated images. To facilitate the meta-learning process, we additionally introduce a consistency-based Pseudo Label Enhancement (PLE) scheme that improves the quality of the model’s own predictions by ensembling predictions f

貿(mào)易

Conference proceedings 2023 Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical application

overbearing

Conference proceedings 2023rnational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023..The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the followin

rheumatism

0302-9743 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023..The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in th

最后一個

Category-Level Regularized Unlabeled-to-Labeled Learning for?Semi-supervised Prostate Segmentation w, semi-supervised learning (SSL) presents an attractive option as it can utilize both limited labeled data and abundant unlabeled data. However, if the local center has limited image collection capability, there may also not be enough unlabeled data for semi-supervised learning to be effective. To o

ADAGE

Devil is in?Channels: Contrastive Single Domain Generalization for?Medical Image Segmentationsue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domain