8 citations · 28 across the 12 of their papers we have counts for
12 papers
Masked conditional variational autoencoders for chromosome straightening
Jingxiong Li, Sunyi Zheng, Zhongyi Shui +9
Karyotyping is of importance for detecting chromosomal aberrations in human disease. However, chromosomes easily appear curved in microscopic images, which prevents cytogeneticists…
Semi-supervised Cell Recognition under Point Supervision
Zhongyi Shui, Yizhi Zhao, Sunyi Zheng +6
Cell recognition is a fundamental task in digital histopathology image analysis. Point-based cell recognition (PCR) methods normally require a vast number of annotations, which is…
PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology
Yuxuan Sun, Chenglu Zhu, Sunyi Zheng +6
As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, of…
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification
Honglin Li, Chenglu Zhu, Yunlong Zhang +5
While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) classification, such a paradigm still faces performance and generali…
ChrSNet: Chromosome Straightening using Self-attention Guided Networks
Sunyi Zheng, Jingxiong Li, Zhongyi Shui +4
Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curve…
Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology
Yunlong Zhang, Yuxuan Sun, Honglin Li +3
When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an e…
AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Haote Xu, Yunlong Zhang, Liyan Sun +3
Anomaly Detection (AD) on medical images enables a model to recognize any type of anomaly pattern without lesion-specific supervised learning. Data augmentation based methods const…
Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis
Yunlong Zhang, Xin Lin, Yihong Zhuang +6
Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches base…
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation
Chenxin Li, Yunlong Zhang, Jiongcheng Li +2
The goal of unsupervised anomaly segmentation (UAS) is to detect the pixel-level anomalies unseen during training. It is a promising field in the medical imaging community, e.g, we…
Consistent Posterior Distributions under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification
Chenxin Li, Yunlong Zhang, Zhehan Liang +3
Retinal artery/vein (A/V) classification is a critical technique for diagnosing diabetes and cardiovascular diseases. Although deep learning based methods achieve impressive result…
Generator Versus Segmentor: Pseudo-healthy Synthesis
Zhang Yunlong, Li Chenxin, Lin Xin +6
This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches b…
Hard Class Rectification for Domain Adaptation
Yunlong Zhang, Changxing Jing, Huangxing Lin +4
Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been wi…