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Tian Luo

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

FAPEX: Fractional Amplitude-Phase Expressor for Robust Cross-Subject Seizure Prediction

Ruizhe Zheng
Lingyan Mao
DINGDING HAN
Tian Luo
Yi Wang
Jing Ding
Yuguo Yu

Precise, generalizable subject-agnostic seizure prediction (SASP) remains a fundamental challenge due to the intrinsic complexity and significant spectral variability of electrophysiologial signals across individuals and recording modalities. We propose \model{FAPEX}, a novel architecture that introduces a learnable \emph{fractional neural frame operator} (FrNFO) for adaptive time–frequency decomposition. Unlike conventional models that exhibit spectral bias toward low frequencies, our FrNFO employs fractional-order convolutions to capture both high and low-frequency dynamics, achieving approximately $10\%$ improvement in F1-score and sensitivity over state-of-the-art baselines. The FrNFO enables the extraction of \emph{instantaneous phase and amplitude representations} that are particularly informative for preictal biomarker discovery and enhance out-of-distribution generalization. \model{FAPEX} further integrates structural state-space modeling and channelwise attention, allowing it to handle heterogeneous electrode montages. Evaluated across 12 benchmarks spanning species (human, rat, dog, macaque) and modalities (Scalp‑EEG, SEEG, ECoG, LFP), \model{FAPEX} consistently outperforms 23 supervised and 10 self-supervised baselines under nested cross-validation, with gains of up to $15\%$ in sensitivity on complex cross-domain scenarios. It further demonstrates superior performance in several external validation cohorts. To our knowledge, these establish \model{FAPEX} as the first epilepsy model to show consistent superiority in SASP, offering a promising solution for discovering epileptic biomarker evidence supporting the existence of a distinct and identifiable preictal state for and clinical translation.

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JBHI Journal 2024 Journal Article

Continuous Refinement-Based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems

Jia Wu
Tian Luo
Jiachen Zeng
Fangfang Gou

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial information more quickly. The cascade structure refines the segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling and cropping of patches during the inference process due to the ultra-high resolution and complex structure of pathology images. This not only increases the cost and computation time but also results in the loss of cellular details and corrupts the global contextual information. This study proposes a Digital Pathology Image Assistance Program (CRSDPI) for medical decision-making systems that is based on continuous improvement. After locating the region of interest using the maximum inter-class variance method, the pictures are preprocessed to account for the impacts of staining inconsistencies and sensitivity variations on the model's performance. Ultimately, we create a two-phase continuously refined segmentation network (TCRNet) by combining an enhanced continuous refinement model with a coarse segmentation network built on a pyramid scene parsing network. The coarse segmentation network introduces an auxiliary loss term to speed up convergence, and the refined model introduces an implicit function to reduce computational cost and reconstruct more details. The TCRNet model refines the target by successively aligning the features without the need to take cascading decoder operations after encoder. Experiments conducted on digital pathology images of breast cancer and osteosarcoma demonstrate the superior prediction accuracy and computational speed of our strategy.

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AAAI Conference 2023 Conference Paper

ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges

Ruizhe Zheng
Jun Li
Yi Wang
Tian Luo
Yuguo Yu

Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.

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ICML Conference 2022 Conference Paper

Learning Augmented Binary Search Trees

Honghao Lin
Tian Luo
David P. Woodruff

A treap is a classic randomized binary search tree data structure that is easy to implement and supports O(log n) expected time access. However, classic treaps do not take advantage of the input distribution or patterns in the input. Given recent advances in algorithms with predictions, we propose pairing treaps with machine advice to form a learning-augmented treap. We are the first to propose a learning-augmented data structure that supports binary search tree operations such as range-query and successor functionalities. With the assumption that we have access to advice from a frequency estimation oracle, we assign learned priorities to the nodes to better improve the treap’s structure. We theoretically analyze the learning-augmented treap’s performance under various input distributions and show that under those circumstances, our learning-augmented treap has stronger guarantees than classic treaps and other classic tree-based data structures. Further, we experimentally evaluate our learned treap on synthetic datasets and demonstrate a performance advantage over other search tree data structures. We also present experiments on real world datasets with known frequency estimation oracles and show improvements as well.

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