113 citations · 123 across the 22 of their papers we have counts for
22 papers
Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images
Liangrui Pan, Jiadi Luo, Yuxuan Xiao +13
Accurate intraoperative and postoperative diagnosis of spread through air spaces (STAS) is essential for guiding surgical decisions and postoperative management in lung cancer. How…
STAMP: Multi-pattern Attention-aware Multiple Instance Learning for STAS Diagnosis in Multi-center Histopathology Images
Liangrui Pan, xiaoyu Li, Guang Zhu +6
Spread through air spaces (STAS) constitutes a novel invasive pattern in lung adenocarcinoma (LUAD), associated with tumor recurrence and diminished survival rates. However, large-…
PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset
Liangrui Pan, Qingchun Liang, Shen Zhao +2
Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognostic assessment. Faced with regiona…
DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset
Liangrui Pan, Xingchen Li, Zhongyi Chen +2
Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, rapidly and accurately obtaining t…
SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis
Liangrui Pan, Xiaoyu Li, Yutao Dou +4
Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathol…
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
Liangrui Pan, Qingchun Liang, Wenwu Zeng +6
Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard…
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation
Liangrui Pan, Mao Huang, Lian Wang +2
Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning,…
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival
Liangrui Pan, Yijun Peng, Yan Li +4
Integrating the different data modalities of cancer patients can significantly improve the predictive performance of patient survival. However, most existing methods ignore the sim…
Opportunities and challenges in the application of large artificial intelligence models in radiology
Liangrui Pan, Zhenyu Zhao, Ying Lu +4
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI…
SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
Liangrui Pan, Yijun Peng, Yan Li +5
Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and signifi…
PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
Liangrui Pan, Dazheng Liu, Zhichao Feng +2
Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subty…
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images
Liangrui Pan, Lian Wang, Zhichao Feng +2
Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality ma…
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
Liangrui Pan, Yutao Dou, Zhichao Feng +2
Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In thi…
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
Liangrui Pan, Xiang Wang, Qingchun Liang +4
Background and Objective: Given the high heterogeneity and clinical diversity of cancer, substantial variations exist in multi-omics data and clinical features across different can…
MGTUNet: An new UNet for colon nuclei instance segmentation and quantification
Liangrui Pan, Lian Wang, Zhichao Feng +3
Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer…
A review of machine learning approaches, challenges and prospects for computational tumor pathology
Liangrui Pan, Zhichao Feng, Shaoliang Peng
Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, an…
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
Liangrui Pan, Hetian Wang, Lian Wang +5
The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a…
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy
Liangrui Pan, Boya Ji, Peng Xi +3
Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view…
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
Liangrui Pan, Peng Zhang, Chalongrat Daengngam +1
In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. How…
Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network
Liangrui Pan, Pronthep Pipitsunthonsan, Chalongrat Daengngam +1
With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major…
Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network
Liangrui Pan, Pronthep Pipitsunthonsan, Chalongrat Daengngam +3
This paper proposes a new framework based on a wavelet transform and deep neural network for identifying noisy Raman spectrum since, in practice, it is relatively difficult to clas…
Noise Reduction Technique for Raman Spectrum using Deep Learning Network
Liangrui Pan, Pronthep Pipitsunthonsan, Peng Zhang +3
In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL…