17 accepted papers
Virtual screening is a critical step in drug discovery, aiming at identifying potential drugs that bind to a specific protein pocket from a large database of molecules. Traditional docking methods are time-consuming, while learning-based approaches supervised by high-precision conformational or affi…
Recently, coarse-to-fine methods for point cloud registration have achieved great success, but few works deeply explore the impact of feature interaction at both coarse and fine scales. By visualizing attention scores and correspondences, we find that existing methods fail to achieve effective featu…
Self-supervised learning has achieved significant success in various fields such as point cloud detection and segmentation. However, self-supervised learning for point cloud registration is less explored. The recently proposed self-supervised learning framework MSC has paved the way for investigatin
Whole Slide Image (WSI) classification has been widely used in pathological diagnosis and prognosis prediction, and it is commonly formulated as a weakly-supervised Multiple Instance Learning (MIL) problem because of the large size of WSIs and the difficulty of obtaining fine-grained annotations. In…
The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of…
Pathological images play a vital role in clinical cancer diagnosis. Computer-aided diagnosis utilized on digital Whole Slide Images (WSIs) has been widely studied. The major challenge of using deep learning models for WSI analysis is the huge size of WSI images and existing methods struggle between…
The recent multi-modality models have achieved great performance in many vision tasks because the extracted features contain the multi-modality knowledge. However, most of the current registration descriptors have only concentrated on local geometric structures. This paper proposes a method to boost…
Bag-based multiple instance learning (MIL) methods have become the mainstream for Whole Slide Image (WSI) classification. However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large num…
Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous learning-based works commonly focus on supervised registration, which have limitations in practice. Recently, with the adv…
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the domain gap between different image modalities, which is ineff…
This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided…
Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve this problem from either a bag classification or…
Building point-to-point dense correspondence between non-rigid shapes is a fundamental and challenging problem. Although functional map-based methods which calculate basis and convert point-wise map to functional map have shown promising performance on meshes, they are not directly applicable to poi
"Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud. Solving this problem is challenging since inlier correspondences of one instance constitute outliers of all the other instances. Existing methods often re…
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We…
3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel…
Three-dimensional rigid point cloud registration has many applications in computer vision and robotics. Local methods tend to fail, causing global methods to be needed, when the relative transformation is large or the overlap ratio is small. Most existing global methods utilize BnB optimization over…