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Found 12 papers, 3 papers with code
Continual Domain Adversarial Adaptation via Double-Head Discriminators
We prove that with the introduction of a pre-trained source-only domain discriminator, the empirical estimation error of $\gH$-divergence related adversarial loss is reduced from the source domain side.
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Deep learning empowers the mainstream medical image segmentation methods.
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans
In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs.
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning
Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs).
A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning
Based on our particle filter inference algorithm, a semi-supervised learn-ing algorithm is utilized for learning tracking network on intermittent labeled frames by variational inference.
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model.
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision.
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation
Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks.
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation
Many segmentation tasks for biomedical images can be modeled as the minimization of an energy function and solved by a class of max-flow and min-cut optimization algorithms.
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models.
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation
In this paper, we use only two kinds of weak labels, i. e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions.
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment.
Ranked #2 on
Cardiovascular MR Segmentaiton
on HVSMR 2016
