Search Results for author:
Found 9 papers, 7 papers with code
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.
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.
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.
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).
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.
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.
