Search Results for author:
Found 29 papers, 16 papers with code
Towards Arbitrary-Scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework via Implicit Self-texture Enhancement
To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in pathology images.
A comprehensive survey on deep active learning in medical image analysis
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets.
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
EndoKED automates the transformation of raw colonoscopy records into image datasets with pixel-level annotation.
PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration
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.
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples.
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need
In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks.
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts.
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance.
Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification
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 number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.
Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain.
Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)
In this paper, we propose a novel deep graph matching-based framework for point cloud registration.
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Specifically, an attention-based bag classifier is used as the teacher network, which is trained with weak bag labels, and an instance classifier is used as the student network, which is trained using the normalized attention scores obtained from the teacher network as soft pseudo labels for the instances in positive bags.
PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration
Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud.
Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis
Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome.
Boosting Point-BERT by Multi-choice Tokens
Masked language modeling (MLM) has become one of the most successful self-supervised pre-training task.
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification
Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs).
POS-BERT: Point Cloud One-Stage BERT Pre-Training
We propose to use the dynamically updated momentum encoder as the tokenizer, which is updated and outputs the dynamic supervision signal along with the training process.
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning
Especially, we develop a variant of ViT for 3D point cloud feature extraction, which also achieves comparable results with existing backbones when combined with our framework, and visualization of the attention maps show that our model does understand the point cloud by combining the global shape information and multiple local structural information, which is consistent with the inspiration of our representation learning method.
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning
In order to encourage different fusion tasks to promote each other and increase the generalizability of the trained network, we integrate the three self-supervised auxiliary tasks by randomly choosing one of them to destroy a natural image in model training.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning.
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
Ranked #3 on
Medical Image Segmentation
on ACDC
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds
In the UDA architecture, an encoder is shared between the networks for the self-supervised task and the main task of point cloud classification or segmentation, so that the encoder can be trained to extract features suitable for both the source and the target domain data.
Robust Point Cloud Registration Framework Based on Deep Graph Matching
In this paper, we propose a novel deep graph matchingbased framework for point cloud registration.
WaveFuse: A Unified Deep Framework for Image Fusion with Discrete Wavelet Transform
We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning.
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem.
Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition
However, the existing global methods are slow for two main reasons: the computational complexity of BnB is exponential to the problem dimensionality (which is six for 3D rigid registration), and the bound evaluation used in BnB is inefficient.
Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search
Three-dimensional rigid point cloud registration has many applications in computer vision and robotics.
Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net
Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer.
