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Article ; Online: Small-Object Sensitive Segmentation Using Across Feature Map Attention.

Sang, Shengtian / Zhou, Yuyin / Islam, Md Tauhidul / Xing, Lei

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 5, Page(s) 6289–6306

Abstract: Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin ... ...

Abstract	Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This article presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects.
Language	English
Publishing date	2023-04-03
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3211171
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Scalable Embedding Based Neural Network Method for Discovering Knowledge From Biomedical Literature.

Sang, Shengtian / Liu, Xiaoxia / Chen, Xiaoyu / Zhao, Di

IEEE/ACM transactions on computational biology and bioinformatics

2022 Volume 19, Issue 3, Page(s) 1294–1301

Abstract: Nowadays, the amount of biomedical literatures is growing at an explosive speed, and much useful knowledge is yet undiscovered in the literature. Classical information retrieval techniques allow to access explicit information from a given collection of ... ...

Abstract	Nowadays, the amount of biomedical literatures is growing at an explosive speed, and much useful knowledge is yet undiscovered in the literature. Classical information retrieval techniques allow to access explicit information from a given collection of information, but are not able to recognize implicit connections. Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting literature. It could significantly support scientific research by identifying new connections between biomedical entities. However, most of the existing approaches to LBD are not scalable and may not be sufficient to detect complex associations in non-directly-connected literature. In this article, we present a model which incorporates biomedical knowledge graph, graph embedding, and deep learning methods for literature-based discovery. First, the relations between biomedical entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Second, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Third, a bidirectional Long Short-Term Memory (BLSTM) network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.
MeSH term(s)	Knowledge ; Knowledge Bases ; Neural Networks, Computer ; Publications ; Research Design
Language	English
Publishing date	2022-06-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2020.3003947
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Self-Detecting and Self-Cleaning Biomimetic Porous Metal-Based Hydrogel for Oil/Water Separation.

Li, Zhaoxin / Sang, Shengtian / Jiang, Shuyue / Chen, Liang / Zhang, Haifeng

ACS applied materials & interfaces

2022 Volume 14, Issue 22, Page(s) 26057–26067

Abstract: Porous materials with super-wetting surfaces (superhydrophilic/underwater superoleophobic) are ideal for oil/water separation. However, the inability to monitor the pollution degree and self-cleaning during the separation process limits their application ...

Abstract	Porous materials with super-wetting surfaces (superhydrophilic/underwater superoleophobic) are ideal for oil/water separation. However, the inability to monitor the pollution degree and self-cleaning during the separation process limits their application in industrial production. In this study, a porous metal-based hydrogel is proposed, inspired by the porous structure of wood. Porous copper foam with nano-Cu(OH)
Language	English
Publishing date	2022-05-24
Publishing country	United States
Document type	Journal Article
ISSN	1944-8252
ISSN (online)	1944-8252
DOI	10.1021/acsami.2c05327
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors.

Liu, Junyan / Islam, Md Tauhidul / Sang, Shengtian / Qiu, Liang / Xing, Lei

NPJ precision oncology

2023 Volume 7, Issue 1, Page(s) 117

Abstract: The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework ...

Abstract	The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.
Language	English
Publishing date	2023-11-06
Publishing country	England
Document type	Journal Article
ISSN	2397-768X
ISSN	2397-768X
DOI	10.1038/s41698-023-00468-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: MKCL: Medical Knowledge with Contrastive Learning model for radiology report generation.

Hou, Xiaodi / Liu, Zhi / Li, Xiaobo / Li, Xingwang / Sang, Shengtian / Zhang, Yijia

Journal of biomedical informatics

2023 Volume 146, Page(s) 104496

Abstract: Automatic radiology report generation has the potential to alert inexperienced radiologists to misdiagnoses or missed diagnoses and improve healthcare delivery efficiency by reducing the documentation workload of radiologists. Motivated by the continuous ...

Abstract	Automatic radiology report generation has the potential to alert inexperienced radiologists to misdiagnoses or missed diagnoses and improve healthcare delivery efficiency by reducing the documentation workload of radiologists. Motivated by the continuous development of automatic image captioning, more and more deep learning methods have been proposed for automatic radiology report generation. However, the visual and textual data bias problem still face many challenges in the medical domain. Additionally, do not integrate medical knowledge, ignoring the mutual influences between medical findings, and abundant unlabeled medical images influence the accuracy of generating report. In this paper, we propose a Medical Knowledge with Contrastive Learning model (MKCL) to enhance radiology report generation. The proposed model MKCL uses IU Medical Knowledge Graph (IU-MKG) to mine the relationship among medical findings and improve the accuracy of identifying positive diseases findings from radiologic medical images. In particular, we design Knowledge Enhanced Attention (KEA), which integrates the IU-MKG and the extracted chest radiological visual features to alleviate textual data bias. Meanwhile, this paper leverages supervised contrastive learning to relieve radiographic medical images which have not been labeled, and identify abnormalities from images. Experimental results on the public dataset IU X-ray show that our proposed model MKCL outperforms other state-of-the-art report generation methods. Ablation studies also demonstrate that IU medical knowledge graph module and supervised contrastive learning module enhance the ability of the model to detect the abnormal parts and accurately describe the abnormal findings. The source code is available at: https://github.com/Eleanorhxd/MKCL.
MeSH term(s)	Humans ; Documentation ; Knowledge ; Radiography ; Radiologists ; Radiology ; Learning
Language	English
Publishing date	2023-09-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2023.104496
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A few-shot sample augmentation algorithm based on SCAM and DEPS for pump fault diagnosis.

Zou, Fengqian / Sang, Shengtian / Jiang, Ming / Guo, Hongliang / Yan, Shaoqing / Li, Xiaoming / Liu, Xiaowei / Zhang, Haifeng

ISA transactions

2023 Volume 142, Page(s) 445–453

Abstract: In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the ... ...

Abstract	In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the connection between performance improvement and the amount of training data remains high, yet real-world samples are difficult to obtain. Combining domain migration theory and sample expansion method, this paper introduces a few-shot learning fault diagnosis method. Employing the T-SNE visualization algorithm, we examine the validity of the self-calibration attention mechanism (SCAM) and distribution edge prediction strategy (DEPS). The accomplishment demonstrated that the proposed algorithm could effectively map the expanded sample space within a separate interval, thereby avoiding the problem of feature aliasing caused by the overlap of sample features among similar categories and significantly enhancing the quality and quantity of training samples. The experimental analysis indicates that the proposed methodology can effectively increase the accuracy of few-shot tasks, especially in the 9way-15shot task, where it maintains a performance of 72 %, which leading the mean accuracy calculated from the others of about 30%. It is believed that much of the work has superior applicability to other few-shot diagnosis cases.
Language	English
Publishing date	2023-07-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2012746-7
ISSN	1879-2022 ; 0019-0578
ISSN (online)	1879-2022
ISSN	0019-0578
DOI	10.1016/j.isatra.2023.07.030
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Self-Detecting and Self-Cleaning Biomimetic Porous Metal-Based Hydrogel for Oil/Water Separation

Li, Zhaoxin / Sang, Shengtian / Jiang, Shuyue / Chen, Liang / Zhang, Haifeng

ACS applied materials & interfaces. 2022 May 24, v. 14, no. 22

2022

Abstract	Porous materials with super-wetting surfaces (superhydrophilic/underwater superoleophobic) are ideal for oil/water separation. However, the inability to monitor the pollution degree and self-cleaning during the separation process limits their application in industrial production. In this study, a porous metal-based hydrogel is proposed, inspired by the porous structure of wood. Porous copper foam with nano-Cu(OH)₂ is used as the skeleton, and its surface is coated with a polyvinyl alcohol, tannic acid, and multiwalled carbon nanotube cross-linked hydrogel coating. The hydrogel has superhydrophilicity and excellent oil/water separation efficiency (>99%) and can adapt to various environments. This approach can also realize hydrogel pollution degree self-detection according to the change in the electrical signal generated during the oil/water separation process, and the hydrogel can also be recovered by soaking to realize self-cleaning. This study will provide new insights into the application of oil/water separation materials in practical industrial manufacturing.
Keywords	biomimetics ; carbon nanotubes ; crosslinking ; foams ; hydrogels ; hydrophilicity ; oils ; pollution ; polyvinyl alcohol ; skeleton ; tannins ; wood
Language	English
Dates of publication	2022-0524
Size	p. 26057-26067.
Publishing place	American Chemical Society
Document type	Article
ISSN	1944-8252
DOI	10.1021/acsami.2c05327
Database	NAL-Catalogue (AGRICOLA)

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Article: Type 1 Diabetes Management With Technology: Patterns of Utilization and Effects on Glucose Control Using Real-World Evidence.

Sun, Ran / Banerjee, Imon / Sang, Shengtian / Joseph, Jennifer / Schneider, Jennifer / Hernandez-Boussard, Tina

Clinical diabetes : a publication of the American Diabetes Association

2021 Volume 39, Issue 3, Page(s) 284–292

Abstract: This retrospective cohort study evaluated diabetes device utilization and the effectiveness of these devices for newly diagnosed type 1 diabetes. Investigators examined the use of continuous glucose monitoring (CGM) systems, self-monitoring of blood ... ...

Abstract	This retrospective cohort study evaluated diabetes device utilization and the effectiveness of these devices for newly diagnosed type 1 diabetes. Investigators examined the use of continuous glucose monitoring (CGM) systems, self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), and multiple daily injection (MDI) insulin regimens and their effects on A1C. The researchers identified 6,250 patients with type 1 diabetes, of whom 32% used CGM and 37.1% used CSII. A higher adoption rate of either CGM or CSII in newly diagnosed type 1 diabetes was noted among White patients and those with private health insurance. CGM users had lower A1C levels than nonusers (
Language	English
Publishing date	2021-08-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1025953-3
ISSN	0891-8929
ISSN	0891-8929
DOI	10.2337/cd20-0098
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Geometric resistant polar quaternion discrete Fourier transform and its application in color image zero-hiding.

Wang, Chunpeng / Ma, Bin / Xia, Zhiqiu / Li, Jian / Li, Qi / Liu, Xiaoxia / Sang, Shengtian

ISA transactions

2021 Volume 125, Page(s) 665–680

Abstract: As a typical frequency-domain analysis method, quaternion discrete Fourier transform (QDFT) has been widely used in information hiding in color images. However, due to the sensitivity of QDFT to geometric attacks, existing QDFT-based information hiding ... ...

Abstract	As a typical frequency-domain analysis method, quaternion discrete Fourier transform (QDFT) has been widely used in information hiding in color images. However, due to the sensitivity of QDFT to geometric attacks, existing QDFT-based information hiding schemes have limited ability in resisting geometric attacks. In this study, a kind of novel geometrically resilient polar QDFT (PQDFT) is constructed and the properties of the proposed PQDFT are analyzed. Subsequently, a PQDFT-based color image zero-hiding scheme robust to geometric attacks is proposed for lossless copyright protection of color images, which experimentally shows reasonable resistance against geometric and common attacks, indicating better robustness compared with the existing QDFT-based information hiding schemes and other leading-edge zero-hiding schemes.
Language	English
Publishing date	2021-06-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2012746-7
ISSN	1879-2022 ; 0019-0578
ISSN (online)	1879-2022
ISSN	0019-0578
DOI	10.1016/j.isatra.2021.06.019
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Sang, Shengtian / Sun, Ran / Coquet, Jean / Carmichael, Harris / Seto, Tina / Hernandez-Boussard, Tina

Journal of medical Internet research

2021 Volume 23, Issue 2, Page(s) e23026

Abstract: Background: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack ...

Abstract	Background: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective: This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods: Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results: Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.
MeSH term(s)	Aged ; Area Under Curve ; COVID-19/diagnosis ; COVID-19/mortality ; Cohort Studies ; Comorbidity ; Female ; Hospitalization/statistics & numerical data ; Humans ; Machine Learning ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/mortality ; Predictive Value of Tests ; Prognosis ; ROC Curve ; Respiration, Artificial/statistics & numerical data ; Retrospective Studies ; SARS-CoV-2 ; Treatment Outcome
Language	English
Publishing date	2021-02-22
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1438-8871
ISSN (online)	1438-8871
ISSN	1438-8871
DOI	10.2196/23026
Database	MEDical Literature Analysis and Retrieval System OnLINE

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