3,443 research outputs found
Inference from medical images with linear discriminant analysis and deep learning-derived features
No full textSeveral challenges arise in the application of modern computer vision and machine learning techniques to making inferences from medical images. They include relatively low sample images, biases inherent in the training data that can act as confounding variables, and the need to incorporate clinical variables about patients. In this dissertation, we combine deep neural networks, pre-trained on large datasets of natural images (e.g. ImageNet) acting as feature extractors, and linear discriminant subspaces to address these issues. We start with our extension to the classic linear discriminant analysis (LDA) to derive multiple mutually orthogonal discriminant directions in the multi-class discriminant subspace, which we refer to as generalised optimal LDA (GO-LDA). Unlike previous work in the field of LDA, the discriminability of the subspace we derive is not limited by the number of classes in the multi-class problem. Empirical work on 14 datasets covering 11 different disease domains shows the advantage of this discriminant subspace approach in a few-shot learning setting. Furthermore, bias in data, arising from confounding information from protected characteristics (e.g. gender or skin tone color), which can lead to unacceptable decision barriers in society, is of serious concern in medical inference problems. Here, we show how an approach biased towards deriving orthogonal discriminant directions, whereby one direction separates the protected characteristic and one separates disease state, can effectively address this issue. This is demonstrated using dermatology and chest X-ray problems in which skin tone color and gender induce confounding issues. Finally, we demonstrate that appending relevant clinical variables in the reduced discriminant subspace is effective, as demonstrated using tuberculosis and dermatology datasets
Beating Classical Impossibility of Position Verification
No full textChandran et al. (SIAM J. Comput. '14) formally introduced the cryptographic task of position verification, where they also showed that it cannot be achieved by classical protocols. In this work, we initiate the study of position verification protocols with classical verifiers. We identify that proofs of quantumness (and thus computational assumptions) are necessary for such position verification protocols. For the other direction, we adapt the proof of quantumness protocol by Brakerski et al. (FOCS '18) to instantiate such a position verification protocol. As a result, we achieve classically verifiable position verification assuming the quantum hardness of Learning with Errors.
Along the way, we develop the notion of 1-of-2 non-local soundness for a natural non-local game for 1-of-2 puzzles, first introduced by Radian and Sattath (AFT '19), which can be viewed as a computational unclonability property. We show that 1-of-2 non-local soundness follows from the standard 2-of-2 soundness (and therefore the adaptive hardcore bit property), which could be of independent interest
sj-docx-1-tct-10.1177_15330338211045510 - Supplemental material for Centromere Protein I (CENP-I) Is Upregulated in Gastric Cancer, Predicts Poor Prognosis, and Promotes Tumor Cell Proliferation and Migration
No full textSupplemental material, sj-docx-1-tct-10.1177_15330338211045510 for Centromere Protein I (CENP-I) Is Upregulated in Gastric Cancer, Predicts Poor Prognosis, and Promotes Tumor Cell Proliferation and Migration by Jiahui Wang, Xin Liu, Hong-jin Chu, Ning Li, Liu-ye Huang and Jian Chen in Technology in Cancer Research & Treatment</p
sj-jpg1-onc-10.1177_11795549221109500 – Supplemental material for Bone-Related Extramedullary Disease in Newly Diagnosed Myeloma Patients is an Independent Poor Prognostic Predictor
No full textSupplemental material, sj-jpg1-onc-10.1177_11795549221109500 for Bone-Related Extramedullary Disease in Newly Diagnosed Myeloma Patients is an Independent Poor Prognostic Predictor by Ying Wang, Aijun Liu, Tingting Xu, Jiahui Yin and Wenming Chen in Clinical Medicine Insights: Oncology</p
Medical image classification by incorporating clinical variables and learned features
No full textMedical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models that only consider single-pixel information, our method captures a more comprehensive view. Our method contains two main steps and is effective in tackling the extra challenge raised by the scarcity of medical data. Firstly, we employ a pre-trained deep neural network served as a feature extractor to capture meaningful image features. Then, an exquisite discriminant analysis is applied to reduce the dimensionality of these features, ensuring that the low number of features remains optimized for the classification task and striking a balance with the clinical variables information. We also develop a way of obtaining class activation maps for our approach in visualizing models' focus on specific regions within the low-dimensional feature space. Thorough experimental results demonstrate improvements of our proposed method over state-of-the-art methods for tuberculosis and dermatology issues for example. Furthermore, a comprehensive comparison with a popular dimensionality reduction technique (principal component analysis) is also conducted.</p
sj-docx-1-pdp-10.1177_10935266231151316 – Supplemental material for Prominent Staining of MYCN Immunohistochemistry Predicts a Poor Prognosis in MYCN Non-Amplified Neuroblastoma
No full textSupplemental material, sj-docx-1-pdp-10.1177_10935266231151316 for Prominent Staining of MYCN Immunohistochemistry Predicts a Poor Prognosis in MYCN Non-Amplified Neuroblastoma by Manli Zhao, Weizhong Gu, Fei Liu, Lihua Yu, Yan Shu, Lei Liu, Jiahui Hu, Yang Liu, Hongfeng Tang and Jianhua Mao in Pediatric and Developmental Pathology</p
sj-docx-1-car-10.1177_19476035231207778 – Supplemental material for Novel-miR-81 Promotes the Chondrocytes Differentiation of Bone Marrow Mesenchymal Stem Cells Through Inhibiting Rac2 Expression
No full textSupplemental material, sj-docx-1-car-10.1177_19476035231207778 for Novel-miR-81 Promotes the Chondrocytes Differentiation of Bone Marrow Mesenchymal Stem Cells Through Inhibiting Rac2 Expression by Ziwei Luo, Jinqi Xie, Haoxiang Ye, Jie Zhang, Yangping Liu, Chunmei Ma, Jiahui Cao, Hao Pan, Xiaosheng Liu, Xianxi Zhou, Jiechen Kong, Dongfeng Chen and Aijun Liu in CARTILAGE</p
sj-tif-2-car-10.1177_19476035231207778 – Supplemental material for Novel-miR-81 Promotes the Chondrocytes Differentiation of Bone Marrow Mesenchymal Stem Cells Through Inhibiting Rac2 Expression
No full textSupplemental material, sj-tif-2-car-10.1177_19476035231207778 for Novel-miR-81 Promotes the Chondrocytes Differentiation of Bone Marrow Mesenchymal Stem Cells Through Inhibiting Rac2 Expression by Ziwei Luo, Jinqi Xie, Haoxiang Ye, Jie Zhang, Yangping Liu, Chunmei Ma, Jiahui Cao, Hao Pan, Xiaosheng Liu, Xianxi Zhou, Jiechen Kong, Dongfeng Chen and Aijun Liu in CARTILAGE</p
Few-shot learning for inference in medical imaging with subspace feature representations
No full textUnlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset problems, of the order of a few hundred items of data, the power of deep learning may still be exploited by using a pre-trained model as a feature extractor and carrying out classic pattern recognition techniques in this feature space, the so-called few-shot learning problem. However, medical images are highly complex and variable, making it difficult for few-shot learning to fully capture and model these features. To address these issues, we focus on the intrinsic characteristics of the data. We find that, in regimes where the dimension of the feature space is comparable to or even larger than the number of images in the data, dimensionality reduction is a necessity and is often achieved by principal component analysis or singular value decomposition (PCA/ SVD). In this paper, noting the inappropriateness of using SVD for this setting we explore two alternatives based on discriminant analysis (DA) and non-negative matrix factorization (NMF). Using 14 different datasets spanning 11 distinct disease types we demonstrate that at low dimensions, discriminant subspaces achieve significant improvements over SVD-based subspaces and the original feature space. We also show that at modest dimensions, NMF is a competitive alternative to SVD in this setting. The implementation of the proposed method is accessible via the following link.</p
Transformational leadership and teachers’ voice behaviour: A moderated mediation model of group voice climate and team psychological safety
No full textTeachers’ voice behaviour has attracted growing attention in universities due to its positive outcomes for institutional reform and improvement. This study investigated how and under what conditions university leaders’ transformational leadership is beneficial to teachers’ voice behaviour using data collected from 434 teachers from universities in China. As a result, we proposed a moderated mediation model of the association between transformational leadership and teachers’ voice behaviour in which group voice climate was used as the mediator and team psychological safety as the moderator. The results revealed evidence of an indirect effect of transformational leadership on teachers’ voice behaviour through the significant mediating role of group voice climate. Moreover, we found evidence that team psychological safety acts as a significant moderator of group voice climate and teachers’ voice behaviour and strengthens the effect of the entire mediating mechanism. Specifically, the mediation effect of group voice climate was significant when team psychological safety levels were at medium or high rather than low levels. Our findings provide a deeper understanding of the benefits and effective mechanism of the impact of transformational leadership on teachers’ voice behaviour in the Chinese university context and offer practical suggestions for facilitating teachers’ voice behaviour in institutions
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