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Yan Han

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5 papers

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JBHI Journal 2025 Journal Article

Channel-Gated Transformers With Affinity CAM for Weakly Supervised Multi-Class Brain Tumor Segmentation

Yan Han
Kai Liu
Lingling Yuan
Md Rahaman
Marcin Grzegorzek
Hongzan Sun
Chen Li
Huiling Chen

Precise tumor localization and sub-region identification are critical for disease diagnosis. However, current Weakly Supervised Semantic Segmentation (WSSS) methods for brain tumor segmentation are primarily single-class, neglecting differences between tumor sub-regions. We observed that when mainstream transformer-based WSSS methods are applied to multi-class brain tumor segmentation, they encounter two major challenges: sub-region discrimination errors and over-segmentation of small lesions. To address these challenges and advance multi-class WSSS methods for brain tumor analysis, this paper proposes Channel-gated Transformers with Affinity CAM (CTAC). CTAC first employs channel-gated multi-head self-attention to overcome the over-smoothing tendency of the transformer, thereby enhancing inter-class discriminability and improving the model's subclass differentiation capability. Then, CTAC uses multi-scale smoothed affinity to adaptively suppress low-confidence responses in the Class Activation Map (CAM), mitigating over-activation in the CAM, and alleviating the over-segmentation phenomena of small lesions. The proposed CTAC significantly outperformed the baseline method on the BraTS2021 glioma and BraTS2023-MEN meningioma datasets. On Brats2021, it achieved a multi-class mean IoU (mIoU) of 61. 718%, an increase of 4. 964 percentage points (pp), with the whole-tumor mIoU reaching 79. 798% (+6. 882 pp). On Brats2023-MEN, CTAC attained 72. 887% mIoU (+4. 676 pp) for multi-class segmentation and 75. 394% (+7. 839 pp) for whole-tumor. Furthermore, CTAC surpasses recent state-of-the-art methods. Code is available at https://github.com/yhan94-lab/CTAC.

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YNIMG Journal 2025 Journal Article

Cortical aperiodic dynamics in hearing impairments predicts neural tracking of speech

Hangze Mao
Yuhan Lu
Zhuang Jiang
Difei Hu
Shuihong Zhou
Xing Tian
Yan Han
Yongtao Xiao

Excitation-inhibition balance is a fundamental property of cortical circuits, reflecting homeostatic plasticity that stabilizes neural activity in the face of functional disruption. This framework has been widely implicated in sensory deprivation and psychiatric disorders. In the auditory domain, it remains unclear how long-term bilateral and unilateral hearing loss reorganizes cortical E-I balance and how such reorganization affects speech processing. Here, we recorded resting-state EEG and measured spectral exponents as a noninvasive proxy for cortical E-I balance in individuals with bilateral hearing loss, single-sided deafness, and normal hearing. We found that spectral exponents differed systematically across hearing loss types. Participants with bilateral hearing loss exhibited reduced exponents, primarily in central-parietal regions, relative to normal-hearing controls, with a gradual increase with prolonged hearing-loss duration. In contrast, left- and right-sided deafness showed distinct patterns of hemispheric lateralization in spectral exponents. Participants also performed a naturalistic speech listening task, allowing quantification of neural tracking of speech. It showed stronger envelope tracking response for bilateral hearing loss group than normal control. Importantly, resting-state exponents across all hearing-impaired groups robustly predicted the strength of speech envelope tracking in noisy environments. These findings reveal dissociable patterns of aperiodic cortical dynamics following bilateral and unilateral auditory deprivation and highlight the homeostatic plasticity in supporting speech perception under challenging listening conditions.

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AIIM Journal 2024 Journal Article

Value function assessment to different RL algorithms for heparin treatment policy of patients with sepsis in ICU

Jiang Liu
Yihao Xie
Xin Shu
Yuwen Chen
Yizhu Sun
Kunhua Zhong
Hao Liang
Yujie Li

Heparin is a critical aspect of managing sepsis after abdominal surgery, which can improve microcirculation, protect organ function, and reduce mortality. However, there is no clinical evidence to support decision-making for heparin dosage. This paper proposes a model called SOFA-MDP, which utilizes SOFA scores as states of MDP, to investigate clinic policies. Different algorithms provide different value functions, making it challenging to determine which value function is more reliable. Due to ethical restrictions, we cannot test all policies on patients. To address this issue, we proposed two value function assessment methods: action similarity rate and relative gain. We experimented with heparin treatment policies for sepsis patients after abdominal surgery using MIMIC-IV. In the experiments, TD ( 0 ) shows the most reliable performance. Using the action similarity rate and relative gain to assess AI policy from TD ( 0 ), the agreement rates between AI policy and “good” physician’s actual treatment are 64. 6% and 73. 2%, while the agreement rates between AI policy and “bad” physician’s actual treatment are 44. 1% and 35. 8%, the gaps are 20. 5% and 37. 4%, respectively. External validation using action similarity rate and relative gain based on eICU resulted in agreement rates of 61. 5% and 69. 1% with the “good” physician’s treatment, and 45. 2% and 38. 3% with the “bad” physician’s treatment, with gaps of 16. 3% and 30. 8%, respectively. In conclusion, the model provides instructive support for clinical decisions, and the evaluation methods accurately distinguish reliable and unreasonable outcomes.

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NeurIPS Conference 2023 Conference Paper

Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling

Haotao Wang
Ziyu Jiang
Yuning You
Yan Han
Gaowen Liu
Jayanth Srinivasa
Ramana Kompella
Zhangyang "Atlas" Wang

Graph neural networks (GNNs) have found extensive applications in learning from graph data. However, real-world graphs often possess diverse structures and comprise nodes and edges of varying types. To bolster the generalization capacity of GNNs, it has become customary to augment training graph structures through techniques like graph augmentations and large-scale pre-training on a wider array of graphs. Balancing this diversity while avoiding increased computational costs and the notorious trainability issues of GNNs is crucial. This study introduces the concept of Mixture-of-Experts (MoE) to GNNs, with the aim of augmenting their capacity to adapt to a diverse range of training graph structures, without incurring explosive computational overhead. The proposed Graph Mixture of Experts (GMoE) model empowers individual nodes in the graph to dynamically and adaptively select more general information aggregation experts. These experts are trained to capture distinct subgroups of graph structures and to incorporate information with varying hop sizes, where those with larger hop sizes specialize in gathering information over longer distances. The effectiveness of GMoE is validated through a series of experiments on a diverse set of tasks, including graph, node, and link prediction, using the OGB benchmark. Notably, it enhances ROC-AUC by $1. 81\%$ in ogbg-molhiv and by $1. 40\%$ in ogbg-molbbbp, when compared to the non-MoE baselines. Our code is publicly available at https: //github. com/VITA-Group/Graph-Mixture-of-Experts.

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AAAI Conference 2023 Conference Paper

PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor

Shun Lu
Yu Hu
Peihao Wang
Yan Han
Jianchao Tan
Jixiang Li
Sen Yang
Ji Liu

Time-consuming performance evaluation is the bottleneck of traditional Neural Architecture Search (NAS) methods. Predictor-based NAS can speed up performance evaluation by directly predicting performance, rather than training a large number of sub-models and then validating their performance. Most predictor-based NAS approaches use a proxy dataset to train model-based predictors efficiently but suffer from performance degradation and generalization problems. We attribute these problems to the poor abilities of existing predictors to character the sub-models' structure, specifically the topology information extraction and the node feature representation of the input graph data. To address these problems, we propose a Transformer-like NAS predictor PINAT, consisting of a Permutation INvariance Augmentation module serving as both token embedding layer and self-attention head, as well as a Laplacian matrix to be the positional encoding. Our design produces more representative features of the encoded architecture and outperforms state-of-the-art NAS predictors on six search spaces: NAS-Bench-101, NAS-Bench-201, DARTS, ProxylessNAS, PPI, and ModelNet. The code is available at https://github.com/ShunLu91/PINAT.

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