158 research outputs found

    Medical Image Analysis using Deep Relational Learning

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    In the past ten years, with the help of deep learning, especially the rapid development of deep neural networks, medical image analysis has made remarkable progress. However, how to effectively use the relational information between various tissues or organs in medical images is still a very challenging problem, and it has not been fully studied. In this thesis, we propose two novel solutions to this problem based on deep relational learning. First, we propose a context-aware fully convolutional network that effectively models implicit relation information between features to perform medical image segmentation. The network achieves the state-of-the-art segmentation results on the Multi Modal Brain Tumor Segmentation 2017 (BraTS2017) and Multi Modal Brain Tumor Segmentation 2018 (BraTS2018) data sets. Subsequently, we propose a new hierarchical homography estimation network to achieve accurate medical image mosaicing by learning the explicit spatial relationship between adjacent frames. We use the UCL Fetoscopy Placenta dataset to conduct experiments and our hierarchical homography estimation network outperforms the other state-of-the-art mosaicing methods while generating robust and meaningful mosaicing result on unseen frames.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0778

    Machine Learning Approaches for Semantic Segmentation on Partly-Annotated Medical Images

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    Semantic segmentation of medical images plays a crucial role in assisting medical practitioners in providing accurate and swift diagnoses; nevertheless, deep neural networks require extensive labelled data to learn and generalise appropriately. This is a major issue in medical imagery because most of the datasets are not fully annotated. Training models with partly-annotated datasets generate plenty of predictions that belong to correct unannotated areas that are categorised as false positives; as a result, standard segmentation metrics and objective functions do not work correctly, affecting the overall performance of the models. In this thesis, the semantic segmentation of partly-annotated medical datasets is extensively and thoroughly studied. The general objective is to improve the segmentation results of medical images via innovative supervised and semi-supervised approaches. The main contributions of this work are the following. Firstly, a new metric, specifically designed for this kind of dataset, can provide a reliable score to partly-annotated datasets with positive expert feedback in their generated predictions by exploiting all the confusion matrix values except the false positives. Secondly, an innovative approach to generating better pseudo-labels when applying co-training with the disagreement selection strategy. This method expands the pixels in disagreement utilising the combined predictions as a guide. Thirdly, original attention mechanisms based on disagreement are designed for two cases: intra-model and inter-model. These attention modules leverage the disagreement between layers (from the same or different model instances) to enhance the overall learning process and generalisation of the models. Lastly, innovative deep supervision methods improve the segmentation results by training neural networks one subnetwork at a time following the order of the supervision branches. The methods are thoroughly evaluated on several histopathological datasets showing significant improvements

    Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation

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    Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%. In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing

    Bayesian sparsity and class sparsity priors for dictionary learning and coding

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    Dictionary learning methods continue to gain popularity for the solution of challenging inverse problems. In the dictionary learning approach, the computational forward model is replaced by a large dictionary of possible outcomes, and the problem is to identify the dictionary entries that best match the data, akin to traditional query matching in search engines. Sparse coding techniques are used to guarantee that the dictionary matching identifies only few of the dictionary entries, and dictionary compression methods are used to reduce the complexity of the matching problem. In this article, we propose a work flow to facilitate the dictionary matching process. First, the full dictionary is divided into subdictionaries that are separately compressed. The error introduced by the dictionary compression is handled in the Bayesian framework as a modeling error. Furthermore, we propose a new Bayesian data-driven group sparsity coding method to help identify subdictionaries that are not relevant for the dictionary matching. After discarding irrelevant subdictionaries, the dictionary matching is addressed as a deflated problem using sparse coding. The compression and deflation steps can lead to substantial decreases of the computational complexity. The effectiveness of compensating for the dictionary compression error and using the novel group sparsity promotion to deflate the original dictionary are illustrated by applying the methodology to real world problems, the glitch detection in the LIGO experiment and hyperspectral remote sensing

    Deep Multi Temporal Scale Networks for Human Motion Analysis

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    The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data. These features are identified and designed by domain experts who can identify the best ones for the problem faced. On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model. In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property. We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards? In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments. Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature

    WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

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    Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

    Classication of Breast Cancer Histopathological Images using Adaptive Penalized Logistic Regression with Wilcoxon Rank Sum Test

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    Classication of the histopathological image is an important problem indiagnosis and treatment. The problem of selecting the most useful fea-tures from thousands of candidates is a key problem in classication of thehistopathological image. In this paper, an adaptive penalized logistic regres-sion is proposed, with the aim of identication features, by combining thelogistic regression with the weighted L1-norm. Our proposed method is ex-perimentally tested and compared with state-of-the-art methods based on apublicly recent breast cancer histopathological image datasets. The resultsshow that the proposed method signicantly outperforms three competitormethods in terms of overall classication accuracy and the number of selectedfeatures

    Computational Histopathology Analysis based on Deep Learning

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    Pathology has benefited from the rapid progress in technology of digital scanning during the last decade. Nowadays, slide scanners are able to produce super-resolution whole slide images (WSI), also called digital slides, which can be explored by image viewers as an alternative to the use of conventional microscope. The use of WSI together with the other microscopic and molecular pathology images brings the development of digital pathology, which further enables to perform digital diagnostics. Moreover, the availability of WSI makes it possible to apply image processing and recognition techniques to support digital diagnostics, opening new revenues of computational pathology. However, there still remain many challenging tasks towards computational pathology such as automated cancer categorisation, tumour area segmentation, and cell-level instance detection. In this study, we explore problems related to the above tasks in histology images. Cancer categorisation can be addressed as a histopathological image classification problem. Multiple aspects such as variations caused by magnification factors and class imbalance make it a challenging task where conventional methods cannot obtain satisfactory performance in many cases. We propose to learn similarity-based embeddings for magnification-independent cancer categorisation. A pair loss and a triplet loss are proposed to learn embeddings that can measure similarity between images for classification. Furthermore, to eliminate the impact of class imbalance, instead of using the strategy of hard samples mining that intuitively discard some easy samples, we introduce a new loss function to simultaneously punish hard misclassified samples and suppress easy well-classified samples. Tumour area segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. Vague boundaries and small regions dissociated from viable tumour areas are two main challenges to accurately segment tumour area. We present a structure-aware scale-adaptive feature selection method for efficient and accurate tumour area segmentation. Specifically, based on a segmentation network with a popular encoder-decoder architecture, a scale-adaptive module is proposed to select more robust features to represent the vague, non-rigid boundaries. Furthermore, a structural similarity metric is proposed for better tissue structure awareness to deal with small region segmentation. Detection of cell-level instances in histology images is essential to acquire morphological and numeric clues for cancer assessment. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. We propose similarity-based region proposal networks for nuclei and cells detection in histology images. In particular, a customized convolution layer termed as embedding layer is designed for network building. The embedding layer is then added on to modify the region proposal networks, which enables the networks to learn discriminative features based on similarity learning
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