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11th KES International Conference on Innovation in Medicine and Healthcare, InMed 2023

作者： Bichindaritz, Isabelle Liu, Guanghui Intelligent BioSystems Lab Biomedical Informatics Department of Computer Science State University of New York at Oswego New York United States

ISBN: (纸本)9789819933105

Survival analysis models the time-to-event information while handling data censorship. Such technique yields extensive applications in carcinoma treatment and prediction. Deep neural networks (DNNs) are appealing for survival analysis because of their non-linear nature. However, DNNs are often described as "black box" models because they are hard or practically impossible to explain. In this study, we propose an explainable deep network framework for survival prediction in cancer. We utilize strategies from nearest neighbor retrieval in case-based reasoning (CBR) to provide useful insights into the inner workings of the deep network. First, we use an autoencoder network to reconstruct the features of a training input. We create a prototype layer, which can store the weight vector following the encoded input and receive the output from the encoder. In this deep survival model network, the total loss function consists of four terms: the negative log partial likelihood function of the Cox model, the autoencoder loss, and two interpretability prototype distance terms. We use an adaptive weights approach to combine the four loss terms. The network that the prototype layer learns during training naturally comes with an explanation for each prediction. We also introduce Shapley Additive Explanation (SHAP) to explain each feature importance for the model predictions. The results of this study demonstrate on two cancer methylation data sets that the developed approach is effective. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Case based reasoning

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding 41

What Improves the Generalization of Graph Transformers? A Th...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony BrookNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10530-10543页

作者： Luyang Luo Yanwen Li Zhizhong Chai Huangjing Lin Pheng-Ann Heng Hao Chen Department of Computer Science and Engineering The Hong Kong University of Science and Technology Sai Kung Hong Kong Department of Biomedical Informatics Harvard University Cambridge MA USA Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Computer Science and Engineering Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://***/poiuohke/SASR_Net .

关键词： Lesions Image segmentation Feature extraction Medical diagnostic imaging Superresolution Diseases Decoding Computed tomography Technological innovation Skin

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What improves the generalization of graph transformers? a theoretical dive into the self-attention and positional encoding 24

What improves the generalization of graph transformers? a th...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongkang Li Meng Wang Tengfei Ma Sijia Liu Zaixi Zhang Pin-Yu Chen Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY Department of Biomedical Informatics Stony Brook University Stony Brook NY Department of Computer Science and Engineering Michigan State University East Lansing MI and MIT-IBM Watson AI Lab IBM Research MA Department of Computer Science and Engineering University of Science and Technology of China Hefei Anhui China IBM Thomas J. Watson Research Center Yorktown Heights NY

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Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation

arXiv

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arXiv 2024年

作者： Gao, Fei Wang, Siwen Zhang, Fandong Zhou, Hong-Yu Wang, Yizhou Wang, Churan Yu, Gang Yu, Yizhou School of Computer Science Peking University Beijing China Deepwise AI Lab Beijing China Department of Biomedical Informatics Harvard Medical School Boston United States CFCS School of Computer Science Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China Children’s Hospital of Zhejiang University School of Medicine Hangzhou China Department of Computer Science The University of Hong Kong Hong Kong

Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SSL) can partially ease this situation by using unlabeled data. However, most existing SSL methods can only make use of data in a single dimensionality (e.g. 2D or 3D), and are incapable of enlarging the training dataset by using data with differing dimensionalities jointly. In this paper, we propose a new cross-dimensional SSL framework based on a pseudo-3D transformation (CDSSL-P3D), that can leverage both 2D and 3D data for joint pre-training. Specifically, we introduce an image transformation based on the im2col algorithm, which converts 2D images into a format consistent with 3D data. This transformation enables seamless integration of 2D and 3D data, and facilitates cross-dimensional self-supervised learning for 3D medical image analysis. We run extensive experiments on 13 downstream tasks, including 2D and 3D classification and segmentation. The results indicate that our CDSSL-P3D achieves superior performance, outperforming other advanced SSL methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels

arXiv

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arXiv 2024年

作者： Xu, Moucheng Zhou, Yukun Goodwin-Allcock, Tobias Firoozabadi, Kimia Jacob, Joseph Alexander, Daniel C. Slator, Paddy J. UCL Hawkes Institute United Kingdom UCL Satsuma Lab United Kingdom UCL Respiratory United Kingdom UCL Department of Computer Science United Kingdom UCL Medical School United Kingdom UCL Department of Medical Physics and Biomedical Engineering United Kingdom Cardiff University School of Computer Science and Informatics United Kingdom Cardiff University Brain Research Imaging Center United Kingdom

We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps that strongly relate to underlying microstructure. Quantitative maps are calculated by fitting a model to multiple images, e.g. with least-squares or machine learning. However, the overwhelming majority of model fitting techniques assume that each voxel is independent, ignoring any co-dependencies in the data. This makes model fitting sensitive to voxelwise measurement noise, hampering reliability and repeatability. We propose a self-supervised deep variational approach that breaks the assumption of independent pixels, leveraging redundancies in the data to effectively perform data-driven regularisation of quantitative maps. We demonstrate that our approach outperforms current model fitting techniques in dMRI simulations and real data. Especially with a Gaussian mixture prior, our model enables sharper quantitative maps, revealing finer anatomical details that are not presented in the baselines. Our approach can hence support the clinical adoption of parameter mapping methods such as dMRI and qMRI. Our code is available at https://***/moucheng2017/MRI-GMM-VAE. © 2024, CC BY-NC-ND.

关键词： Mapping

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding

arXiv

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arXiv 2024年

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown Heights NY United States

关键词： Stochastic systems

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences

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Computational Biology and Chemistry 2022年第0期98卷 107638-107638页

作者： Sattar, Mohsin Majid, Abdul Kausar, Nabeela Bilal, Muhammad Kashif, Muhammad Biomedical Informatics Research Lab Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan MIS Division Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan Department of Computing and Technology Iqra University Islamabad Pakistan

Lung cancer is one of the leading causes of cancer related deaths. Early diagnosis of lung cancer using automatic feature selection from large number of features is a challenging task. Conventionally, cancer diagnosis approaches use physical features that appear in later stages, while harmful effects have already been occurred due to abnormal somatic mutations. In order to extract useful novel patterns to efficiently predict cancer at early stages, we analyzed lung cancer related mutated genes that reveal useful information in protein amino acid sequences. For this, we developed a new evolutionary learning technique with biologically inspired multi-gene genetic programming algorithm using discriminant information of protein amino acids. The proposed model efficiently selects 23 discriminant features out of 1500 features. Then it combines the selected features and related primitive functions optimally for prediction of lung cancer. Hence, an efficient predictive model is constructed that helps in understanding the complex heterogeneous nature of lung cancer. The proposed system achieved area under ROC curve and accuracy values of 98.79% and 95.67%, respectively outperforming related lung cancer prediction approaches. © 2022 Elsevier Ltd

关键词： Proteins

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Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications

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Yearbook of Medical informatics 2021年第1期30卷 219-225页

作者： McGrath, Scott P. Benton, Mary Lauren Tavakoli, Maryam Tatonetti, Nicholas P. CITRIS Health University of California Berkeley Berkeley CA USA Department of Computer Science Baylor University TX Waco United States MTERMS Lab Brigham and Women's Hospital Harvard Medical School MA Boston United States Department of Biomedical Informatics Columbia University NY NY United States

OBJECTIVES: Provide an overview of the emerging themes and notable papers which were published in 2020 in the field of Bioinformatics and Translational informatics (BTI) for the International Medical informatics Association Yearbook. METHODS: A team of 16 individuals scanned the literature from the past year. Using a scoring rubric, papers were evaluated on their novelty, importance, and objective quality. 1,224 Medical Subject Headings (MeSH) terms extracted from these papers were used to identify themes and research focuses. The authors then used the scoring results to select notable papers and trends presented in this manuscript. RESULTS: The search phase identified 263 potential papers and central themes of coronavirus disease 2019 (COVID-19), machine learning, and bioinformatics were examined in greater detail. CONCLUSIONS: When addressing a once in a centruy pandemic, scientists worldwide answered the call, with informaticians playing a critical role. Productivity and innovations reached new heights in both TBI and science, but significant research gaps remain. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://***/licenses/by-nc-nd/4.

关键词： bioinformatics COVID-19 machine learning

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