activity
20212023
most cited3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis

23 citations · 25 across the 9 of their papers we have counts for

collaborators

15 papers

q-bio.QM20241 cited

Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View

Jianan Fan, Dongnan Liu, Canran Li +5

Cellular nuclei recognition serves as a fundamental and essential step in the workflow of digital pathology. However, with disparate source organs and staining procedures among his…

cs.CV2024

White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging

Yui Lo, Yuqian Chen, Fan Zhang +6

Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype clas…

cs.CV20241 cited

LaPA: Latent Prompt Assist Model For Medical Visual Question Answering

Tiancheng Gu, Kaicheng Yang, Dongnan Liu +1

Medical visual question answering (Med-VQA) aims to automate the prediction of correct answers for medical images and questions, thereby assisting physicians in reducing repetitive…

cs.LG2024

Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling

Jianan Fan, Dongnan Liu, Hang Chang +3

Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of…

cs.CV2024

Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation

Jianan Fan, Dongnan Liu, Hang Chang +1

Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles hindering the application of deep learning models for nuclei analysis, which holds a b…

cs.CV20231 cited

Complex Organ Mask Guided Radiology Report Generation

Tiancheng Gu, Dongnan Liu, Zhiyuan Li +1

The goal of automatic report generation is to generate a clinically accurate and coherent phrase from a single given X-ray image, which could alleviate the workload of traditional…