Search Results for author:
Found 9 papers, 8 papers with code
Generating Progressive Images from Pathological Transitions via Diffusion Model
Deep learning is widely applied in computer-aided pathological diagnosis, which alleviates the pathologist workload and provide timely clinical analysis.
PuzzleTuning: Explicitly Bridge Pathological and Natural Image with Puzzles
Pathological image analysis is a crucial field in computer vision.
CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training
This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization.
CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification
In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task.
Shuffle Instances-based Vision Transformer for Pancreatic Cancer ROSE Image Classification
The rapid on-site evaluation (ROSE) technique can signifi-cantly accelerate the diagnosis of pancreatic cancer by im-mediately analyzing the fast-stained cytopathological images.
Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances
Computer-aided diagnosis (CAD) using the deep learning method has the potential to solve the problem of insufficient pathology staffing.
MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer
Pancreatic cancer is one of the most malignant cancers in the world, which deteriorates rapidly with very high mortality.
Prior Attention Network for Multi-Lesion Segmentation in Medical Images
The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks.
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above.
