Search Results for author:
Found 19 papers, 16 papers with code
Temporal Consistent Automatic Video Colorization via Semantic Correspondence
Extensive experiments demonstrate that our method outperforms other methods in maintaining temporal consistency both qualitatively and quantitatively.
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS) based on image-level labels is challenging since it is hard to obtain complete semantic regions.
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan.
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning
At the inter-domain level, we propose a cross-domain alignment loss to help the model use the target prototype for cross-domain knowledge transfer.
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models.
TCNL: Transparent and Controllable Network Learning Via Embedding Human-Guided Concepts
In TCNL, the shallow feature extractor gets preliminary features first.
WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels
For scenarios where weak supervision and cross-domain problems coexist, this paper defines a new task: unsupervised domain adaptation based on weak source domain labels (WUDA).
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.
Ranked #1 on
Image-to-Image Translation
on BCI
Breast Cancer Detection
Classification Of Breast Cancer Histology Images
+4
Hard Sample Aware Noise Robust Learning for Histopathology Image Classification
In this work, we introduce a novel hard sample aware noise robust learning method for histopathology image classification.
Ranked #1 on
Learning with noisy labels
on Chaoyang
Construct Informative Triplet with Two-stage Hard-sample Generation
In this paper, we propose a robust sample generation scheme to construct informative triplets.
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance.
Ranked #1 on
Learning with noisy labels
on CIFAR-100N
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
However, the previous approaches did not fully exploit information of hard samples, simply using cluster centroid or all instances for contrastive learning.
LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
It is very challenging for various visual tasks such as image fusion, pedestrian detection and image-to-image translation in low light conditions due to the loss of effective target areas.
Ranked #1 on
Thermal Infrared Pedestrian Detection
on LLVIP
Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark
In recent years, deep learning-based methods have shown promising results in computer vision area.
Ranked #1 on
Scene Text Recognition
on MSDA
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation.
Ranked #13 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate.
Ranked #1 on
Tumor Segmentation
on DigestPath
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
Experimental results on glomeruli segmentation from renal biopsy images indicate that our network is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data.
Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
In this paper, we propose a novel method for highly efficient follicular segmentation of thyroid cytopathological WSIs.
