A5053569630- Advanced Neural Network Applications
- Domain Adaptation and Few-Shot Learning
- Image Retrieval and Classification Techniques
- Image Processing Techniques and Applications
- Machine Learning and ELM
- Particle Detector Development and Performance
- AI in cancer detection
- Robot Manipulation and Learning
- Robotic Mechanisms and Dynamics
- Digital Imaging for Blood Diseases
South China University of Technology
2022-2023
Whole-slide image (WSI) classification is fundamental to computational pathology, which challenging in extra-high resolution, expensive manual annotation, data heterogeneity, etc. Multiple instance learning (MIL) provides a promising way towards WSI classification, nevertheless suffers from the memory bottleneck issue inherently, due gigapixel high resolution. To avoid this issue, overwhelming majority of existing approaches have decouple feature encoder and MIL aggregator networks, may...
Nucleus instance segmentation from histopathology images suffers the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial etc. In paper, we propose formulate perspective few-shot (FSL). Our work was motivated by that, with prosperity computational pathology, an increasing number...
Few-shot learning (FSL) usually assumes that the query is drawn from same label space as support set, while queries unknown classes may emerge unexpectedly in many open-world application scenarios. Such an open-set issue will limit practical deployment of FSL systems, which remains largely unexplored. In this paper, we investigate problem few-shot recognition (FSOR) and propose a novel solution, called Relative Feature Displacement Network (RFDNet), empowers systems to reject accurately...
Nucleus instance segmentation from histopathology images suffers the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial etc. In paper, we propose formulate perspective few-shot (FSL). Our work was motivated by that, with prosperity computational pathology, an increasing number...
To tackle the "reality gap" encountered in Sim-to-Real transfer, this study proposes a diffusion-based framework that minimizes inconsistencies grasping actions between simulation settings and realistic environments. The process begins by training an adversarial supervision layout-to-image diffusion model(ALDM). Then, leverage ALDM approach to enhance environment, rendering it with photorealistic fidelity, thereby optimizing robotic grasp task training. Experimental results indicate...