20 citations · 30 across the 11 of their papers we have counts for
7 papers · 1 filter
Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Wenliang Zhong, Rob Barton, Lucas Goncalves +7
Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the…
Text-Guided Multi-Instance Learning for Scoliosis Screening via Gait Video Analysis
Haiqing Li, Yuzhi Guo, Feng Jiang +5
Early-stage scoliosis is often difficult to detect, particularly in adolescents, where delayed diagnosis can lead to serious health issues. Traditional X-ray-based methods carry ra…
Leveraging Gait Patterns as Biomarkers: An attention-guided Deep Multiple Instance Learning Network for Scoliosis Classification
Haiqing Li, Yuzhi Guo, Feng Jiang +3
Scoliosis is a spinal curvature disorder that is difficult to detect early and can compress the chest cavity, impacting respiratory function and cardiac health. Especially for adol…
Histopathology Multi-modal Embedding for Pathology Composed Retrieval
Qifeng Zhou, Wenliang Zhong, Thao M. Dang +4
To overcome the black-box nature of predictive AI and the hallucination risks of generative models, retrieval-based models offer an interpretable, evidence-based paradigm for patho…
Compositional Image Retrieval via Instruction-Aware Contrastive Learning
Wenliang Zhong, Weizhi An, Feng Jiang +3
Composed Image Retrieval (CIR) involves retrieving a target image based on a composed query of an image paired with text that specifies modifications or changes to the visual refer…
PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning
Qifeng Zhou, Wenliang Zhong, Yuzhi Guo +3
In the field of computational histopathology, both whole slide images (WSIs) and diagnostic captions provide valuable insights for making diagnostic decisions. However, aligning WS…
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
Jinyu Yang, Chunyuan Li, Weizhi An +5
Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network…