Multi-task deep learning for medical image computing and analysis: A review
The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …
conventional deep learning models are constructed for a single specific task, multi-task deep …
Learning from multiple datasets with heterogeneous and partial labels for universal lesion detection in CT
Large-scale datasets with high-quality labels are desired for training accurate deep learning
models. However, due to the annotation cost, datasets in medical imaging are often either …
models. However, due to the annotation cost, datasets in medical imaging are often either …
SAM: Self-supervised learning of pixel-wise anatomical embeddings in radiological images
Radiological images such as computed tomography (CT) and X-rays render anatomy with
intrinsic structures. Being able to reliably locate the same anatomical structure across …
intrinsic structures. Being able to reliably locate the same anatomical structure across …
[HTML][HTML] Joint learning framework of cross-modal synthesis and diagnosis for Alzheimer's disease by mining underlying shared modality information
Alzheimer's disease (AD) is one of the most common neurodegenerative disorders
presenting irreversible progression of cognitive impairment. How to identify AD as early as …
presenting irreversible progression of cognitive impairment. How to identify AD as early as …
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
Establishing dense anatomical correspondence across distinct imaging modalities is a
foundational yet challenging procedure for numerous medical image analysis studies and …
foundational yet challenging procedure for numerous medical image analysis studies and …
Learning better registration to learn better few-shot medical image segmentation: Authenticity, diversity, and robustness
In this work, we address the task of few-shot medical image segmentation (MIS) with a novel
proposed framework based on the learning registration to learn segmentation (LRLS) …
proposed framework based on the learning registration to learn segmentation (LRLS) …
Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration
The main objective of anatomically plausible results for deformable image registration is to
improve model's registration accuracy by minimizing the difference between a pair of fixed …
improve model's registration accuracy by minimizing the difference between a pair of fixed …
SAME: Deformable image registration based on self-supervised anatomical embeddings
In this work, we introduce a fast and accurate method for unsupervised 3D medical image
registration. This work is built on top of a recent algorithm self-supervised anatomical …
registration. This work is built on top of a recent algorithm self-supervised anatomical …
CoCycleReg: Collaborative cycle-consistency method for multi-modal medical image registration
Multi-modal image registration is an essential step for many medical image analysis
applications. Recent advances in multi-modal image registration rely on image-to-image …
applications. Recent advances in multi-modal image registration rely on image-to-image …
Review of Generative Adversarial Networks in mono-and cross-modal biomedical image registration
T Han, J Wu, W Luo, H Wang, Z **… - Frontiers in …, 2022 - frontiersin.org
Biomedical image registration refers to aligning corresponding anatomical structures among
different images, which is critical to many tasks, such as brain atlas building, tumor growth …
different images, which is critical to many tasks, such as brain atlas building, tumor growth …