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  1. Article ; Online: NIR ditriphenylamine Indole-BODIPY photosensitizer: synthesis, photodynamic therapy in A549 cells and two-photon fluorescence imaging in zebrafish.

    Liu, Ruibo / Qian, Ying

    Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

    2023  Volume 304, Page(s) 123387

    Abstract: In this study, the ditriphenylamine Indole-BODIPY photosensitizer ... ...

    Abstract In this study, the ditriphenylamine Indole-BODIPY photosensitizer T
    Language English
    Publishing date 2023-09-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 210413-1
    ISSN 1873-3557 ; 0370-8322 ; 0584-8539 ; 1386-1425
    ISSN (online) 1873-3557
    ISSN 0370-8322 ; 0584-8539 ; 1386-1425
    DOI 10.1016/j.saa.2023.123387
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

    Z 51/98: Show issues
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    924/340: Show issues

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  2. Book ; Online: Knolling bot

    Hu, Yuhang / Zhang, Zhizhuo / Liu, Ruibo / Wyder, Philippe / Lipson, Hod

    A Transformer-based Approach to Organizing a Messy Table

    2023  

    Abstract: In this study, we propose an approach to equip domestic robots with the ability to perform simple household tidying tasks. We focus specifically on 'knolling,' an activity related to organizing scattered items into neat and space-efficient arrangements. ... ...

    Abstract In this study, we propose an approach to equip domestic robots with the ability to perform simple household tidying tasks. We focus specifically on 'knolling,' an activity related to organizing scattered items into neat and space-efficient arrangements. Unlike the uniformity of industrial environments, household settings present unique challenges due to their diverse array of items and the subjectivity of tidiness. Here, we draw inspiration from natural language processing (NLP) and utilize a transformer-based approach that predicts the next position of an item in a sequence of neatly positioned items. We integrate the knolling model with a visual perception model and a physical robot arm to demonstrate a machine that declutters and organizes a dozen freeform items of various shapes and sizes.

    Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
    Keywords Computer Science - Robotics ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 629
    Publishing date 2023-10-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Dilated transformer: residual axial attention for breast ultrasound image segmentation.

    Shen, Xiaoyan / Wang, Liangyu / Zhao, Yu / Liu, Ruibo / Qian, Wei / Ma, He

    Quantitative imaging in medicine and surgery

    2022  Volume 12, Issue 9, Page(s) 4512–4528

    Abstract: Background: The segmentation of breast ultrasound (US) images has been a challenging task, mainly due to limited data and the inherent image characteristics involved, such as low contrast and speckle noise. Although convolutional neural network-based ( ... ...

    Abstract Background: The segmentation of breast ultrasound (US) images has been a challenging task, mainly due to limited data and the inherent image characteristics involved, such as low contrast and speckle noise. Although convolutional neural network-based (CNN-based) methods have made significant progress over the past decade, they lack the ability to model long-range interactions. Recently, the transformer method has been successfully applied to the tasks of computer vision. It has a strong ability to capture distant interactions. However, most transformer-based methods with excellent performance rely on pre-training on large datasets, making it infeasible to directly apply them to medical images analysis, especially that of breast US images with limited high-quality labels. Therefore, it is of great significance to find a robust and efficient transformer-based method for use on small breast US image datasets.
    Methods: We developed a dilated transformer (DT) method which mainly uses the proposed residual axial attention layers to build encoder blocks and the introduced dilation module (DM) to further increase the receptive field. We evaluated the proposed method on 2 breast US image datasets using the 5-fold cross-validation method. Dataset A was a public dataset with 562 images, while dataset B was a private dataset with 878 images. Ground truth (GT) was delineated by 2 radiologists with more than 5 years of experience. The evaluation was followed by related ablation experiments.
    Results: The DT was found to be comparable with the state-of-the-art (SOTA) CNN-based method and outperformed the related transformer-based method, medical transformer (MT), on both datasets. Especially on dataset B, the DT outperformed the MT on metrics of Jaccard index (JI) and Dice similarity coefficient (DSC) by 2.67% and 4.68%, respectively. Meanwhile, when compared with Unet, the DT improved JI and DSC by 4.89% and 4.66%, respectively. Moreover, the results of the ablation experiments showed that each add-on part of the DT is important and contributes to the segmentation accuracy.
    Conclusions: The proposed transformer-based method could achieve advanced segmentation performance on different small breast US image datasets without pretraining.
    Language English
    Publishing date 2022-07-21
    Publishing country China
    Document type Journal Article
    ZDB-ID 2653586-5
    ISSN 2223-4306 ; 2223-4292
    ISSN (online) 2223-4306
    ISSN 2223-4292
    DOI 10.21037/qims-22-33
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Accurate segmentation of breast tumor in ultrasound images through joint training and refined segmentation.

    Shen, Xiaoyan / Wu, Xinran / Liu, Ruibo / Li, Hong / Yin, Jiandong / Wang, Liangyu / Ma, He

    Physics in medicine and biology

    2022  Volume 67, Issue 17

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Algorithms ; Breast Neoplasms/diagnostic imaging ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Ultrasonography ; Ultrasonography, Mammary
    Language English
    Publishing date 2022-09-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ac8964
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 199: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Book ; Online: Non-Parallel Text Style Transfer with Self-Parallel Supervision

    Liu, Ruibo / Gao, Chongyang / Jia, Chenyan / Xu, Guangxuan / Vosoughi, Soroush

    2022  

    Abstract: The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style ... ...

    Abstract The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style transfer models thus only receive weak supervision of the target sentences during training, which often leads the model to discard too much style-independent information, or utterly fail to transfer the style. In this work, we propose LaMer, a novel text style transfer framework based on large-scale language models. LaMer first mines the roughly parallel expressions in the non-parallel datasets with scene graphs, and then employs MLE training, followed by imitation learning refinement, to leverage the intrinsic parallelism within the data. On two benchmark tasks (sentiment & formality transfer) and a newly proposed challenging task (political stance transfer), our model achieves qualitative advances in transfer accuracy, content preservation, and fluency. Further empirical and human evaluations demonstrate that our model not only makes training more efficient, but also generates more readable and diverse expressions than previous models.

    Comment: In ICLR 2022
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-04-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Language Model Augmented Relevance Score

    Liu, Ruibo / Wei, Jason / Vosoughi, Soroush

    2021  

    Abstract: Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. ...

    Abstract Although automated metrics are commonly used to evaluate NLG systems, they often correlate poorly with human judgements. Newer metrics such as BERTScore have addressed many weaknesses in prior metrics such as BLEU and ROUGE, which rely on n-gram matching. These newer methods, however, are still limited in that they do not consider the generation context, so they cannot properly reward generated text that is correct but deviates from the given reference. In this paper, we propose Language Model Augmented Relevance Score (MARS), a new context-aware metric for NLG evaluation. MARS leverages off-the-shelf language models, guided by reinforcement learning, to create augmented references that consider both the generation context and available human references, which are then used as additional references to score generated text. Compared with seven existing metrics in three common NLG tasks, MARS not only achieves higher correlation with human reference judgements, but also differentiates well-formed candidates from adversarial samples to a larger degree.

    Comment: In ACL 2021
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning
    Subject code 401
    Publishing date 2021-08-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Modulating Language Models with Emotions

    Liu, Ruibo / Wei, Jason / Jia, Chenyan / Vosoughi, Soroush

    2021  

    Abstract: Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated layer normalization -- a technique inspired by computer vision -- that ... ...

    Abstract Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated layer normalization -- a technique inspired by computer vision -- that allows us to use large-scale language models for emotional response generation. In automatic and human evaluation on the MojiTalk dataset, our proposed modulated layer normalization method outperforms prior baseline methods while maintaining diversity, fluency, and coherence. Our method also obtains competitive performance even when using only 10% of the available training data.

    Comment: Findings of ACL 2021
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning
    Publishing date 2021-08-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Political Depolarization of News Articles Using Attribute-aware Word Embeddings

    Liu, Ruibo / Wang, Lili / Jia, Chenyan / Vosoughi, Soroush

    2021  

    Abstract: Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper, we focus on addressing one of the factors that contributes to this ... ...

    Abstract Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper, we focus on addressing one of the factors that contributes to this polarity, polarized media. We introduce a framework for depolarizing news articles. Given an article on a certain topic with a particular ideological slant (eg., liberal or conservative), the framework first detects polar language in the article and then generates a new article with the polar language replaced with neutral expressions. To detect polar words, we train a multi-attribute-aware word embedding model that is aware of ideology and topics on 360k full-length media articles. Then, for text generation, we propose a new algorithm called Text Annealing Depolarization Algorithm (TADA). TADA retrieves neutral expressions from the word embedding model that not only decrease ideological polarity but also preserve the original argument of the text, while maintaining grammatical correctness. We evaluate our framework by comparing the depolarized output of our model in two modes, fully-automatic and semi-automatic, on 99 stories spanning 11 topics. Based on feedback from 161 human testers, our framework successfully depolarized 90.1% of paragraphs in semi-automatic mode and 78.3% of paragraphs in fully-automatic mode. Furthermore, 81.2% of the testers agree that the non-polar content information is well-preserved and 79% agree that depolarization does not harm semantic correctness when they compare the original text and the depolarized text. Our work shows that data-driven methods can help to locate political polarity and aid in the depolarization of articles.

    Comment: In Proceedings of the 15th International AAAI Conference on Weblogs and Social Media (ICWSM 2021)
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 400
    Publishing date 2021-01-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Second Thoughts are Best

    Liu, Ruibo / Jia, Chenyan / Zhang, Ge / Zhuang, Ziyu / Liu, Tony X / Vosoughi, Soroush

    Learning to Re-Align With Human Values from Text Edits

    2023  

    Abstract: We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through ... ...

    Abstract We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thought not only achieves superior performance in three value alignment benchmark datasets but also shows strong human-value transfer learning ability in few-shot scenarios. The generated editing steps also offer better interpretability and ease for interactive error correction. Extensive human evaluations further confirm its effectiveness.

    Comment: In proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Publishing date 2023-01-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Lesion segmentation in breast ultrasound images using the optimized marked watershed method.

    Shen, Xiaoyan / Ma, He / Liu, Ruibo / Li, Hong / He, Jiachuan / Wu, Xinran

    Biomedical engineering online

    2021  Volume 20, Issue 1, Page(s) 57

    Abstract: Background: Breast cancer is one of the most serious diseases threatening women's health. Early screening based on ultrasound can help to detect and treat tumours in the early stage. However, due to the lack of radiologists with professional skills, ... ...

    Abstract Background: Breast cancer is one of the most serious diseases threatening women's health. Early screening based on ultrasound can help to detect and treat tumours in the early stage. However, due to the lack of radiologists with professional skills, ultrasound-based breast cancer screening has not been widely used in rural areas. Computer-aided diagnosis (CAD) technology can effectively alleviate this problem. Since breast ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD systems, is challenging.
    Results: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 open-source BUS images. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model RDAU-NET. Its accuracy (Acc), Dice similarity coefficient (DSC) and Jaccard index (JI) reached 96.25%, 78.4% and 65.34% on dataset A, and its Acc, DSC and sensitivity reached 97.96%, 86.25% and 88.79% on dataset B, respectively.
    Conclusions: We proposed an adaptive morphological snake based on marked watershed (AMSMW) algorithm for BUS image segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions.
    Methods: The proposed method consists of two steps. In the first step, contrast limited adaptive histogram equalization (CLAHE) and a side window filter (SWF) are used to preprocess BUS images. Lesion contours can be effectively highlighted, and the influence of noise can be eliminated to a great extent. In the second step, we propose adaptive morphological snake (AMS). It can adjust the working parameters adaptively according to the size of the lesion. Its segmentation results are combined with those of the morphological method. Then, we determine the marked area and obtain candidate contours with a marked watershed (MW). Finally, the best lesion contour is chosen by the maximum average radial derivative (ARD).
    MeSH term(s) Algorithms ; Female ; Humans ; Ultrasonography, Mammary
    Language English
    Publishing date 2021-06-07
    Publishing country England
    Document type Journal Article
    ZDB-ID 2084374-4
    ISSN 1475-925X ; 1475-925X
    ISSN (online) 1475-925X
    ISSN 1475-925X
    DOI 10.1186/s12938-021-00891-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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