Search Results for author:
Found 14 papers, 4 papers with code
A Dataset and Model for Realistic License Plate Deblurring
Vehicle license plate recognition is a crucial task in intelligent traffic management systems.
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively.
Ranked #1 on
Medical Image Segmentation
on 2018 Data Science Bowl
Geometry-aware Single-image Full-body Human Relighting
For the relighting, we introduce a ray tracing-based per-pixel lighting representation that explicitly models high-frequency shadows and propose a learning-based shading refinement module to restore realistic shadows (including hard cast shadows) from the ray-traced shading maps.
Mitosis domain generalization in histopathology images -- The MIDOG challenge
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge
We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images.
Class-Aware Domain Adaptation for Improving Adversarial Robustness
To improve the adversarial robustness of neural networks, adversarial training has been proposed to train networks by injecting adversarial examples into the training data.
K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information.
Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task.
An anomaly prediction framework for financial IT systems using hybrid machine learning methods
To improve the efficiency and accuracy of system failure detection and thereby reduce the impact of system failures on financial services, we propose a novel machine learning-based framework to predict the occurrence of system exceptions and failures in a financial software system.
Learning Deep Image Priors for Blind Image Denoising
In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment.
Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification
In this paper, we present a novel deep hybrid attention approach to breast cancer classification.
Discovering Influential Factors in Variational Autoencoder
In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks.
Outline Objects using Deep Reinforcement Learning
Image segmentation needs both local boundary position information and global object context information.
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray
To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score.
