Search Results for author:
Found 25 papers, 10 papers with code
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation
When activating class objects, we argue that the false activation stems from the bias to the ambiguous regions during the feature extraction.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
In this work, we devise a 'Separate and Conquer' scheme SeCo to tackle this issue from dimensions of image space and feature space.
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.
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.
Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy.
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning.
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.
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)
In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis.
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.
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.
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).
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.
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.
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.
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.
Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling
Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment.
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.
