Chen Sun

• Master of Engineering
• PhD Student at University of Toronto

STUDY QUESTION Can a quantitative method be developed to differentiate between blastocysts with similar or same inner cell mass (ICM) and trophectoderm (TE) grades, while also reflecting their potential for live birth? SUMMARY ANSWER We developed BlastScoringNet, an interpretable deep-learning model that quantifies blastocyst ICM and TE morphology...

Zero-shot anomaly detection (ZSAD) aims to develop a foundational model capable of detecting anomalies across arbitrary categories without relying on reference images. However, since “abnormality” is inherently defined in relation to “normality” within specific categories, detecting anomalies without reference images describing the corresponding no...

Sperm morphology measurement is vital for diagnosing male infertility, which involves quantification of multiple subcellular parts for each sperm. Instance-aware part segmentation networks have been introduced to address this task by automatically identifying individual sperm and segmenting their subcellular parts. However, major limitations of sta...

Image anomaly localization is a pivotal technique in industrial inspection, often manifesting as a supervised task where abundant normal samples coexist with rare abnormal samples. Existing supervised methods in this context are prone to overfitting, as they primarily encounter anomalies that represent only a fraction of the open-world anomalies. C...

Anomaly detection is a crucial task across different domains and data types. However, existing anomaly detection models are often designed for specific domains and modalities. This study explores the use of GPT-4V(ision), a powerful visual-linguistic model, to address anomaly detection tasks in a generic manner. We investigate the application of GP...

This technical report introduces the winning solution of the team \textit{Segment Any Anomaly} for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, \textit{e.g.}, language prompt, we present a novel framework, \textit{i.e.}, Segment Any Anomaly + (SAA$+$), for zero-shot anomaly segmentation with mul...

We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentation models typically rely on domain-specific fine-tuning, limiting their generalization across countless anomaly patterns. In this w...

Defect detection is one of the most essential processes for industrial quality inspection. However, in Continuous Defect Detection (CDD), where defect categories and samples continually increase, the challenge of incremental few-shot defect detection remains unexplored. Current defect detection models fail to generalize to novel categories and suff...

Surface defect detection is one of the most essential processes for industrial quality inspection. Deep learning-based surface defect detection methods have shown great potential. However, the well-performed models usually require large training data and can only detect defects that appeared in the training stage. When facing incremental few-shot d...

Particle defects on the cathodic copper plate surface always happen due to the immaturity of electrolytic copper processing. The removal of defects mainly depends on their height exceeding the plate and current removal requires manual measurement and operation, which is time-consuming and laborious. To automate the removal process, machine vision-b...