5 citations · 10 across the 15 of their papers we have counts for
13 papers · 1 filter
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Haiwen Diao, Penghao Wu, Hanming Deng +55
Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fra…
RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
Yaxuan Song, Jianan Fan, Tianyi Wang +4
Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular architecture and functional progra…
Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos
Jieyun Bai, Zihao Zhou, Yitong Tang +60
A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income c…
ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rays
Qinyi Cao, Jianan Fan, Weidong Cai
Unsupervised anomaly detection aims to identify anomalies without pixel-level annotations. Synthetic anomaly-based methods exhibit a unique capacity to introduce controllable irreg…
SurgiATM: A Physics-Guided Plug-and-Play Model for Deep Learning-Based Smoke Removal in Laparoscopic Surgery
Mingyu Sheng, Jianan Fan, Dongnan Liu +3
During laparoscopic surgery, smoke generated by tissue cauterization can significantly degrade the visual quality of endoscopic frames, increasing the risk of surgical errors and h…
Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images
Yaxuan Song, Jianan Fan, Hang Chang +1
Accurately predicting gene expression from histopathology images offers a scalable and non-invasive approach to molecular profiling, with significant implications for precision med…
Towards Conditional Feature Alignment for Cross-Domain Counting
Zhuonan Liang, Dongnan Liu, Jianan Fan +6
Object counting models often degrade under cross-domain deployment because density composition varies across domains and is itself task-relevant. Standard feature alignment methods…
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention
Tianyi Wang, Jianan Fan, Dingxin Zhang +4
Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. Multi-modal self-supervised learnin…
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
Hao Xu, Tengfei Xue, Jianan Fan +7
Medical image registration is a fundamental task in medical image analysis, aiming to establish spatial correspondences between paired images. However, existing unsupervised deform…
AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation
Mingyu Sheng, Jianan Fan, Dongnan Liu +2
Surgical instrument segmentation (SIS) is pivotal for robotic-assisted minimally invasive surgery, assisting surgeons by identifying surgical instruments in endoscopic video frames…
Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View
Mingyu Sheng, Jianan Fan, Dongnan Liu +2
Surgical instrument segmentation (SIS) on endoscopic images stands as a long-standing and essential task in the context of computer-assisted interventions for boosting minimally in…
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
Yaxuan Song, Jianan Fan, Dongnan Liu +1
Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy. However, exist…
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…