Anatomical invariance modeling and semantic alignment for self-supervised learning in 3d medical image analysis
Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally�…
image analysis tasks. Most current methods follow existing SSL paradigm originally�…
Voco: A simple-yet-effective volume contrastive learning framework for 3d medical image analysis
Abstract Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical
image analysis. However the lack of high-level semantics in pre-training still heavily hinders�…
image analysis. However the lack of high-level semantics in pre-training still heavily hinders�…
Models genesis: Generic autodidactic models for 3d medical image analysis
Transfer learning from natural image to medical image has established as one of the most
practical paradigms in deep learning for medical image analysis. However, to fit this�…
practical paradigms in deep learning for medical image analysis. However, to fit this�…
Geometric visual similarity learning in 3d medical image self-supervised pre-training
Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training,
due to their sharing of numerous same semantic regions. However, the lack of the semantic�…
due to their sharing of numerous same semantic regions. However, the lack of the semantic�…
Models genesis
Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm�…
most practical paradigms in deep learning for medical image analysis. To fit this paradigm�…
Self-supervised pre-training of swin transformers for 3d medical image analysis
Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream�…
learning of global and local representations that can be transferred to downstream�…
Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?
Discriminative, restorative, and adversarial learning have proven beneficial for self-
supervised learning schemes in computer vision and medical imaging. Existing efforts�…
supervised learning schemes in computer vision and medical imaging. Existing efforts�…
Stepwise incremental pretraining for integrating discriminative, restorative, and adversarial learning
We have developed a United framework that integrates three self-supervised learning (SSL)
ingredients (discriminative, restorative, and adversarial learning), enabling collaborative�…
ingredients (discriminative, restorative, and adversarial learning), enabling collaborative�…
Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis
F Haghighi, MRH Taher…�- Proceedings of the�…, 2022 - openaccess.thecvf.com
Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging�…
beneficial for self-supervised learning schemes in computer vision and medical imaging�…
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability Composability and Decomposability from Anatomy via Self Supervision
MRH Taher, MB Gotway…�- Proceedings of the IEEE�…, 2024 - openaccess.thecvf.com
Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep
learning excels in learning multi-level feature spaces but they often lack explicit coding of�…
learning excels in learning multi-level feature spaces but they often lack explicit coding of�…