Point cloud registration is a task to estimate the rigid transformation
...
Active learning (AL) is an effective approach to select the most informa...
Weakly supervised whole slide image classification is usually formulated...
This paper introduces the novel concept of few-shot weakly supervised
le...
Existing super-resolution models for pathology images can only work in f...
The recent multi-modality models have achieved great performance in many...
Unsupervised domain adaptation (UDA) aims to learn a model trained on so...
3D point cloud registration is a fundamental problem in computer vision ...
Computer-aided pathology diagnosis based on the classification of Whole ...
Multi-instance point cloud registration is the problem of estimating mul...
Histopathological images contain abundant phenotypic information and
pat...
Masked language modeling (MLM) has become one of the most successful
sel...
Multiple Instance Learning (MIL) is widely used in analyzing
histopathol...
Recently, the pre-training paradigm combining Transformer and masked lan...
In this paper, we propose a simple and general framework for self-superv...
Image fusion is a technique to integrate information from multiple sourc...
As real-scanned point clouds are mostly partial due to occlusions and
vi...
In this paper, we propose TransMEF, a transformer-based multi-exposure i...
Deep neural networks have achieved promising performance in supervised p...
3D point cloud registration is a fundamental problem in computer vision ...
We propose an unsupervised image fusion architecture for multiple applic...
Absolute pose estimation is a fundamental problem in computer vision, an...
The rigid registration of two 3D point sets is a fundamental problem in
...
Accurate segmentation of organ at risk (OAR) play a critical role in the...