45 citations · 69 across the 10 of their papers we have counts for
10 papers
KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis
Qixiang Zhang, Yi Li, Tianqi Xiang +4
Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated into a slide representation,…
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62
Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings acro…
Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
Sirui Zhang, Haonan Wang, Xunkai Li +5
Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to support various network applicat…
Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding
Yuchen Wang, Haonan Wang, Yu Guo +2
Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain constrained by three fundamental issues…
Token Activation Map to Visually Explain Multimodal LLMs
Yi Li, Hualiang Wang, Xinpeng Ding +2
Multimodal large language models (MLLMs) are broadly empowering various fields. Despite their advancements, the explainability of MLLMs remains less explored, hindering deeper unde…
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
Haonan Wang, Qixiang Zhang, Yi Li +1
Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along wit…
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Haonan Wang, Peng Cao, Xiaoli Liu +2
Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-loc…
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
Haonan Wang, Xiaomeng Li
Volume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) technique…
DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
Haonan Wang, Xiaomeng Li
The volume-wise labeling of 3D medical images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL) is highly desirable for training with limited labeled d…
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Haonan Wang, Peng Cao, Jiaqi Wang +1
Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to mo…