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学者姓名：李辰
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Abstract ：
Cancer region detection (CRD) and subtyping are two fundamental tasks in digital pathology image analysis. The development of data-driven models for CRD and subtyping on whole-slide images (WSIs) would mitigate the burden of pathologists and improve their accuracy in diagnosis. However, the existing models are facing two major limitations. Firstly, they typically require large-scale datasets with precise annotations, which contradicts with the original intention of reducing labor effort. Secondly, for the subtyping task, the non-cancerous regions are treated as the same as cancerous regions within a WSI, which confuses a subtyping model in its training process. To tackle the latter limitation, the previous research proposed to perform CRD first for ruling out the non-cancerous region, then train a subtyping model based on the remaining cancerous patches. However, separately training ignores the interaction of these two tasks, also leads to propagating the error of the CRD task to the subtyping task. To address these issues and concurrently improve the performance on both CRD and subtyping tasks, we propose a semi-supervised multi-task learning (MTL) framework for cancer classification. Our framework consists of a backbone feature extractor, two task-specific classifiers, and a weight control mechanism. The backbone feature extractor is shared by two task-specific classifiers, such that the interaction of CRD and subtyping tasks can be captured. The weight control mechanism preserves the sequential relationship of these two tasks and guarantees the error back-propagation from the subtyping task to the CRD task under the MTL framework. We train the overall framework in a semi-supervised setting, where datasets only involve small quantities of annotations produced by our minimal point-based (min-point) annotation strategy. Extensive experiments on four large datasets with different cancer types demonstrate the effectiveness of the proposed framework in both accuracy and generalization. © 2022 Elsevier B.V.
Keyword ：
Cancer region detection; Cancer subtyping; Computational pathology; Min-point annotation; Multi-task learning; Semi-supervised learning
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| GB/T 7714 | Gao, Z. , Hong, B. , Li, Y. et al. A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images [J]. | Medical Image Analysis , 2023 , 83 . |
| MLA | Gao, Z. et al. "A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images" . | Medical Image Analysis 83 (2023) . |
| APA | Gao, Z. , Hong, B. , Li, Y. , Zhang, X. , Wu, J. , Wang, C. et al. A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images . | Medical Image Analysis , 2023 , 83 . |
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Abstract ：
Extracting knowledge from heterogeneous data sources is fundamental for the construction of structured biomedical knowledge graphs (BKGs), where entities and relations are represented as nodes and edges in the graphs, respectively. Previous biomedical knowledge extraction methods simply considered limited entity types and relations by using a task-specific training set, which is insufficient for large-scale BKGs development and downstream task applications in different scenarios. To alleviate this issue, we propose a joint continual learning biomedical information extraction (JCBIE) network to extract entities and relations from different biomedical information datasets. By empirically studying different joint learning and continual learning strategies, the proposed JCBIE can learn and expand different types of entities and relations from different datasets. JCBIE uses two separated encoders in joint-feature extraction, hence can effectively avoid the feature confusion problem comparing with using one hard-parameter sharing encoder. Specifically, it allows us to adopt entity augmented inputs to establish the interaction between named entity recognition and relation extraction. Finally, a novel evaluation mechanism is proposed for measuring cross-corpus generalization errors, which was ignored by traditional evaluation methods. Our empirical studies show that JCBIE achieves promising performance when continual learning strategy is adopted with multiple corpora.
Keyword ：
Biomedical information extraction Continual learning Joint learning
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| GB/T 7714 | He, Kai , Mao, Rui , Gong, Tieliang et al. JCBIE: a joint continual learning neural network for biomedical information extraction [J]. | BMC BIOINFORMATICS , 2022 , 23 (1) . |
| MLA | He, Kai et al. "JCBIE: a joint continual learning neural network for biomedical information extraction" . | BMC BIOINFORMATICS 23 . 1 (2022) . |
| APA | He, Kai , Mao, Rui , Gong, Tieliang , Cambria, Erik , Li, Chen . JCBIE: a joint continual learning neural network for biomedical information extraction . | BMC BIOINFORMATICS , 2022 , 23 (1) . |
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Abstract ：
Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outliers and heavy-tailed noises. Understanding modal regression's theoretical behavior can be fundamental in learning theory. Despite significant progress in characterizing its statistical property, the majority of the results are based on the assumption that samples are independent and identical distributed (i.i.d.), which is too restrictive for real-world applications. This paper concerns the statistical property of regularized modal regression (RMR) within an important dependence structure - Markov dependent. Specifically, we establish the upper bound for RMR estimator under moderate conditions and give an explicit learning rate. Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain. This result shed a new light on characterizing the theoretical underpinning for robust regression.
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| GB/T 7714 | Gong, Tieliang , Dong, Yuxin , Chen, Hong et al. Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment [J]. | THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE , 2022 : 6721-6728 . |
| MLA | Gong, Tieliang et al. "Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment" . | THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (2022) : 6721-6728 . |
| APA | Gong, Tieliang , Dong, Yuxin , Chen, Hong , Feng, Wei , Dong, Bo , Li, Chen . Regularized Modal Regression on Markov-Dependent Observations: A Theoretical Assessment . | THE TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE , 2022 , 6721-6728 . |
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Abstract ：
The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.
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| GB/T 7714 | Lou, Peiliang , Wang, Chunbao , Guo, Ruifeng et al. HistoML, a markup language for representation and exchange of histopathological features in pathology images [J]. | SCIENTIFIC DATA , 2022 , 9 (1) . |
| MLA | Lou, Peiliang et al. "HistoML, a markup language for representation and exchange of histopathological features in pathology images" . | SCIENTIFIC DATA 9 . 1 (2022) . |
| APA | Lou, Peiliang , Wang, Chunbao , Guo, Ruifeng , Yao, Lixia , Zhang, Guanjun , Yang, Jun et al. HistoML, a markup language for representation and exchange of histopathological features in pathology images . | SCIENTIFIC DATA , 2022 , 9 (1) . |
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Abstract ：
Subsampling is an important technique to tackle the computational challenges brought by big data. Many subsampling procedures fall within the framework of importance sampling, which assigns high sampling probabilities to the samples appearing to have big impacts. When the noise level is high, those sampling procedures tend to pick many outliers and thus often do not perform satisfactorily in practice. To tackle this issue, we design a new Markov subsampling strategy based on Huber criterion (HMS) to construct an informative subset from the noisy full data; the constructed subset then serves as refined working data for efficient processing. HMS is built upon a Metropolis-Hasting procedure, where the inclusion probability of each sampling unit is determined using the Huber criterion to prevent over scoring the outliers. Under mild conditions, we show that the estimator based on the subsamples selected by HMS is statistically consistent with a sub-Gaussian deviation bound. The promising performance of HMS is demonstrated by extensive studies on large-scale simulations and real data examples.
Keyword ：
Convergence Data models Estimation Markov chain Markov processes Noise measurement regression robust inference subsampling Task analysis TV
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| GB/T 7714 | Gong, Tieliang , Dong, Yuxin , Chen, Hong et al. Markov Subsampling Based on Huber Criterion [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 . |
| MLA | Gong, Tieliang et al. "Markov Subsampling Based on Huber Criterion" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022) . |
| APA | Gong, Tieliang , Dong, Yuxin , Chen, Hong , Dong, Bo , Li, Chen . Markov Subsampling Based on Huber Criterion . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2022 . |
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Abstract ：
Tissue segmentation is an essential task in computational pathology. However, relevant datasets for such a pixel-level classification task are hard to obtain due to the difficulty of annotation, bringing obstacles for training a deep learning-based segmentation model. Recently, contrastive learning has provided a feasible solution for mitigating the heavy reliance of deep learning models on annotation. Nevertheless, applying contrastive loss to the most abstract image representations, existing contrastive learning frameworks focus on global features, therefore, are less capable of encoding finer-grained features (e.g., pixel-level discrimination) for the tissue segmentation task. Enlightened by domain knowledge, we design three contrastive learning tasks with multi-granularity views (from global to local) for encoding necessary features into representations without accessing annotations. Specifically, we construct: (1) an image-level task to capture the difference between tissue components, i.e., encoding the component discrimination; (2) a superpixel-level task to learn discriminative representations of local regions with different tissue components, i.e., encoding the prototype discrimination; (3) a pixel-level task to encourage similar representations of different tissue components within a local region, i.e., encoding the spatial smoothness. Through our global-to-local pre-training strategy, the learned representations can reasonably capture the domain-specific and fine-grained patterns, making them easily transferable to various tissue segmentation tasks in histopathological images. We conduct extensive experiments on two tissue segmentation datasets, while considering two real-world scenarios with limited or sparse annotations. The experimental results demonstrate that our framework is superior to existing contrastive learning methods and can be easily combined with weakly supervised and semi-supervised segmentation methods. © 1982-2012 IEEE.
Keyword ：
Contrastive learning; histopathological image; superpixel; tissue segmentation
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| GB/T 7714 | Gao, Z. , Jia, C. , Li, Y. et al. Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast [J]. | IEEE Transactions on Medical Imaging , 2022 , 41 (12) : 3611-3623 . |
| MLA | Gao, Z. et al. "Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast" . | IEEE Transactions on Medical Imaging 41 . 12 (2022) : 3611-3623 . |
| APA | Gao, Z. , Jia, C. , Li, Y. , Zhang, X. , Hong, B. , Wu, J. et al. Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast . | IEEE Transactions on Medical Imaging , 2022 , 41 (12) , 3611-3623 . |
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Abstract ：
Motivation: Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events' attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results: In this article, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences.
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| GB/T 7714 | Li, Yufei , Ma, Xiaoyong , Zhou, Xiangyu et al. Knowledge enhanced LSTM for coreference resolution on biomedical texts [J]. | BIOINFORMATICS , 2021 , 37 (17) : 2699-2705 . |
| MLA | Li, Yufei et al. "Knowledge enhanced LSTM for coreference resolution on biomedical texts" . | BIOINFORMATICS 37 . 17 (2021) : 2699-2705 . |
| APA | Li, Yufei , Ma, Xiaoyong , Zhou, Xiangyu , Cheng, Pengzhen , He, Kai , Li, Chen . Knowledge enhanced LSTM for coreference resolution on biomedical texts . | BIOINFORMATICS , 2021 , 37 (17) , 2699-2705 . |
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Abstract ：
The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained nuclei classification into two cross-category classification tasks that are leaned by two newly designed high-resolution feature extractors (HRFEs). The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited to the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset.
Keyword ：
Histopathology Nuclei grading Nuclei segmentation
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| GB/T 7714 | Gao, Zeyu , Shi, Jiangbo , Zhang, Xianli et al. Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network [C] . 2021 : 132-142 . |
| MLA | Gao, Zeyu et al. "Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network" . (2021) : 132-142 . |
| APA | Gao, Zeyu , Shi, Jiangbo , Zhang, Xianli , Li, Yang , Zhang, Haichuan , Wu, Jialun et al. Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network . (2021) : 132-142 . |
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Abstract ：
Background: Genealogical information, such as that found in family trees, is imperative for biomedical research such as disease heritability and risk prediction. Researchers have used policyholder and their dependent information in medical claims data and emergency contacts in electronic health records (EHRs) to infer family relationships at a large scale. We have previously demonstrated that online obituaries can be a novel data source for building more complete and accurate family trees. Objective: Aiming at supplementing EHR data with family relationships for biomedical research, we built an end-to-end information extraction system using a multitask-based artificial neural network model to construct genealogical knowledge graphs (GKGs) from online obituaries. GKGs are enriched family trees with detailed information including age, gender, death and birth dates, and residence. Methods: Built on a predefined family relationship map consisting of 4 types of entities (eg, people's name, residence, birth date, and death date) and 71 types of relationships, we curated a corpus containing 1700 online obituaries from the metropolitan area of Minneapolis and St Paul in Minnesota. We also adopted data augmentation technology to generate additional synthetic data to alleviate the issue of data scarcity for rare family relationships. A multitask-based artificial neural network model was then built to simultaneously detect names, extract relationships between them, and assign attributes (eg, birth dates and death dates, residence, age, and gender) to each individual. In the end, we assemble related GKGs into larger ones by identifying people appearing in multiple obituaries. Results: Our system achieved satisfying precision (94.79%), recall (91.45%), and F-1 measures (93.09%) on 10-fold cross-validation. We also constructed 12,407 GKGs, with the largest one made up of 4 generations and 30 people. Conclusions: In this work, we discussed the meaning of GKGs for biomedical research, presented a new version of a corpus with a predefined family relationship map and augmented training data, and proposed a multitask deep neural system to construct and assemble GKGs. The results show our system can extract and demonstrate the potential of enriching EHR data for more genetic research. We share the source codes and system with the entire scientific community on GitHub without the corpus for privacy protection.
Keyword ：
EHR genealogical knowledge graph genealogy information extraction neural network
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| GB/T 7714 | He, Kai , Yao, Lixia , Zhang, JiaWei et al. Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System [J]. | JOURNAL OF MEDICAL INTERNET RESEARCH , 2021 , 23 (8) . |
| MLA | He, Kai et al. "Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System" . | JOURNAL OF MEDICAL INTERNET RESEARCH 23 . 8 (2021) . |
| APA | He, Kai , Yao, Lixia , Zhang, JiaWei , Li, Yufei , Li, Chen . Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System . | JOURNAL OF MEDICAL INTERNET RESEARCH , 2021 , 23 (8) . |
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Abstract ：
Multi-class geospatial object detection in remote sensing images suffer great challenges, such as large scales variability and complex background. Although feature pyramid network (FPN) can alleviate the problem of scale variation to some extent, it causes the loss of spatial and semantic information which is not conducive to object location. To address the above problem, this paper proposes a discriminative feature pyramid network (DFPN) by introducing a global guidance module (GGM) and a feature aggregation module (FAM). Specifically, the global guidance module delivers the high-level semantic information to lower layers, so as to obtain feature maps with stronger semantic information to eliminate the interference caused by complex background. The feature aggregation module enhances the interfiow of information between different layers and better captures the discrimination information at each layer. We validate the effectiveness of our method on the NWPU VHR-10 and RSOD datasets, the results outperform baseline by 2.06 and 3.88 points respectively.
Keyword ：
discriminative feature learning feature aggregation module global guidance module Object detection
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| GB/T 7714 | Zhu, Xiaoqian , Zhang, Xiangrong , Zhang, Tianyang et al. Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images [C] . 2020 . |
| MLA | Zhu, Xiaoqian et al. "Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images" . (2020) . |
| APA | Zhu, Xiaoqian , Zhang, Xiangrong , Zhang, Tianyang , Zhu, Peng , Tang, Xu , Li, Chen . Discriminative Feature Pyramid Network For Object Detection In Remote Sensing Images . (2020) . |
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