Search Results for author:
Found 13 papers, 10 papers with code
Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-annotated data with diverse base classes.
Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary
Domain generalization typically requires data from multiple source domains for model learning.
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use.
DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample.
Histopathological Image Classification
Image Classification
+2
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels.
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
This paper studies a practical yet challenging FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i. e., hospitals).
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection.
Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries.
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation.
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle.
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.
Unpaired Multi-modal Segmentation via Knowledge Distillation
We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy.
