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

bourgeois

OpenAL: An Efficient Deep Active Learning Framework for?Open-Set Pathology Image Classificationification of pathology images show that OpenAL can significantly improve the query quality of target class samples and achieve higher performance than current state-of-the-art AL methods. Code is available at ..

碌碌之人

瑪瑙

COLosSAL: A Benchmark for?Cold-Start Active Learning for?3D Medical Image Segmentationark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public Medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on diff

Aerophagia

Continual Learning for?Abdominal Multi-organ and?Tumor Segmentationto accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pre

Expurgate

Incremental Learning for?Heterogeneous Structure Segmentation in?Brain Tumor MRIcal categories in a unified manner. Specifically, we first propose a divergence-aware dual-flow module with balanced rigidity and plasticity branches to decouple old and new tasks, which is guided by continuous batch renormalization. Then, a complementary pseudo-label training scheme with self-entro

Outwit

臆斷

Adapter Learning in?Pretrained Feature Extractor for?Continual Learning of?Diseasesegory, task-specific adapter(s) can help the pretrained feature extractor more effectively extract discriminative features between diseases. In addition, a simple yet effective fine-tuning is applied to collaboratively fine-tune multiple task-specific heads such that outputs from different heads are

鋼筆尖

COST

VISA-FSS: A Volume-Informed Self Supervised Approach for?Few-Shot 3D Segmentatione performance of 3D medical segmentation. To achieve this goal, we introduce a volume-aware task generation method that utilizes consecutive slices within a 3D image to construct more varied and realistic self-supervised FSS tasks during training. In addition, to provide pseudo-labels for consecutiv

戰(zhàn)勝