20 citations · 30 across the 11 of their papers we have counts for
13 papers
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…
scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
Qifeng Zhou, Lei Yu, Yuzhi Guo +5
The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we introduce scpFormer, a transfor…
GenePheno: Interpretable Gene Knockout-Induced Phenotype Abnormality Prediction from Gene Sequences
Jingquan Yan, Yuwei Miao, Lei Yu +4
Exploring how genetic sequences shape phenotypes is a fundamental challenge in biology and a key step toward scalable, hypothesis-driven experimentation. The task is complicated by…
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…
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence
Feng Jiang, Mangal Prakash, Hehuan Ma +6
Molecular property prediction aims to learn representations that map chemical structures to functional properties. While multimodal learning has emerged as a powerful paradigm to l…
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…
GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction
Yuwei Miao, Yuzhi Guo, Hehuan Ma +4
Exploring the functions of genes and gene products is crucial to a wide range of fields, including medical research, evolutionary biology, and environmental science. However, disco…
UniEntrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation Benchmarks with Unified Entrez Gene Identifiers
Yuwei Miao, Yuzhi Guo, Hehuan Ma +5
Gene studies are crucial for fields such as protein structure prediction, drug discovery, and cancer genomics, yet they face challenges in fully utilizing the vast and diverse info…
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…
Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
Saiyang Na, Yuzhi Guo, Feng Jiang +2
In the rapidly evolving field of AI research, foundational models like BERT and GPT have significantly advanced language and vision tasks. The advent of pretrain-prompting models s…
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…