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In recent times, bladder cancer has increased significantly in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC). In this work, we focus on the MIBC subtype because it has the worst prognosis and can spread to adjacent organs. We present a self-learning framework to grade bladder cancer from histological images stained by immunohistochemical techniques. Specifically, we propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows for the classification of histological patches into different levels of disease severity, according to established patterns in the literature. The proposed DCEAC model follows a fully unsupervised two-step learning methodology to discern between non-tumour, mild and infiltrative patterns from high-resolution 512 × 512 pixel samples. Our system outperforms previous clustering-based methods by including a convolutional attention module, which enables the refinement of the features of the latent space prior to the classification stage. The proposed network surpasses state-of-the-art approaches by 2-3% across different metrics, reaching a final average accuracy of 0.9034 in a multi-class scenario. Furthermore, the reported class activation maps evidence that our model is able to learn by itself the same patterns that clinicians consider relevant, without requiring previous annotation steps. This represents a breakthrough in MIBC grading that bridges the gap with respect to training the model on labelled data., (Copyright © 2021. Published by Elsevier Ltd.)

Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods. [ABSTRACT FROM AUTHOR]

Unsupervised clustering, which is extensively employed in deep learning and computer vision as a fundamental technique, has attracted much attention in recent years. Deep embedding clustering often uses auto-encoders to learn representations for clustering. However, auto-encoders tend to corrupt the learning representations when simultaneously learning embedded representations and performing clustering. In this paper, we propose a Deep Clustering via Variational Auto-Encoder (DC-VAE) of mutual information maximization. First, we formulate the deep clustering problem as learning soft cluster assignments within the framework of variational auto-encoder. Second, we impose mutual information maximization on the observed data and the representations to prevent soft cluster assignments from distorting learning representations. Third, we derive a new generalization evidence lower bound objects related to several previous models and introduce parameters to balance learning informative representations and clustering. It is shown that the proposed model can significantly boost the performance of clustering by learning effective and reliable representations for downstream machine learning tasks. Through experimental results on several datasets, we demonstrate that the proposed model is competitive with existing state-of-the-arts on multiple performance metrics. [ABSTRACT FROM AUTHOR]

Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks. In addition, the initial cluster centers in the learned feature space are generated by k -means. This only works well on spherical clusters and probably leads to unstable clustering results. In this paper, we propose a two-stage deep density-based image clustering (DDC) framework to address these issues. The first stage is to train a deep convolutional autoencoder (CAE) to extract low-dimensional feature representations from high-dimensional image data, and then apply t-SNE to further reduce the data to a 2-dimensional space favoring density-based clustering algorithms. In the second stage, we propose a novel density-based clustering technique for the 2-dimensional embedded data to automatically recognize an appropriate number of clusters with arbitrary shapes. Concretely, a number of local clusters are generated to capture the local structures of clusters, and then are merged via their density relationship to form the final clustering result. Experiments demonstrate that the proposed DDC achieves comparable or even better clustering performance than state-of-the-art deep clustering methods, even though the number of clusters is not given. [ABSTRACT FROM AUTHOR]

Patient stratification has made a great contribution to efficient and personalized medicine. An important task in patient stratification is to discover quite distinct disease subtypes for effective treatment. In this paper, we propose a new deep learning and clustering model which combines Deep Belief Network (DBN) and Fuzzy C-Means(FCM), called Unsupervised Deep Fuzzy C-Means clustering Network(UDFCMN), to cluster lung cancer patients from lung CT images. In our deep clustering network, images after preprocessing are first encoded into multiple layers of hidden variables to extract hierarchical features and feature distribution and form the high-level representations. Here, to solve the problem of feature homogenization in DBN, we introduce the Winner-Take-All (WTA) idea to meliorate the traditional DBN structure, called WTADBN. Then FCM is used to produce the initial cluster labels with the new representations learnt by stacked WTARBM. Therefore, the FCM-generated cluster labels are used for the fine-tuning of the DBN as ground-truth labels. And an unsupervised image clustering and patient stratification process is completed by cross iteration. We tested our deep FCM clustering algorithm to do experiment on both public dataset from the internet and private dataset from cooperate hospital. For the latter one, the clinical and biological verification was also performed. Experimental results reveal outperformance of UDFCMN as compared to the state-of-the-art unsupervised classification methods. These results also indicate that our approach may have practical applications in lung cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy. • Unsupervised learning can learn hidden features related to survival in images. • Mutations in lung cancer related genes can be shown on CT images. • The proposed model can distinguish clinical difference among lung cancer subtypes. • The stratification of patients helps physicians with important clinical decisions. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) is an indispensable technique in computer vision and pattern recognition Existing deep IQA methods have achieved remarkable performance. As far as we know, these deep learning-based IQA algorithms lack an adaptive features extraction mechanism toward input images with varying sizes and the stability in avoiding disturbance from data noises and model deviation. To solve these problems, we propose a non-reference IQA method by designing a novel unsupervised deep clustering framework, where a 13-layer network structure is proposed that upgrades the fully-connected layers to produce high-level features with adaptive sizes. Moreover, we add a contracted regular term with a contracted autoencoder into the clustering loss function to form a quality model reflecting the clustering structure. Compared to the other IQA algorithms, our model with simple structure exhibits more stable and robust performance by the initial configuration of network parameters during end-to-end training. The experimental results on the LIVE and CSIP databases have shown that our method not only performs better than the state-of-the-art IQA algorithms, but also has a simpler structure and better adaptability. • The proposed framework is adaptive for IQA on images with varying size. • We incorporate a contract autoencoder to loss function to ensure that the results of IQA has a better stability, robustness, and anti-disturbance. • Provide that: our proposed CDEC achieves a better image quality evaluation performance than the three ablation models. [ABSTRACT FROM AUTHOR]

Objective . A previous study has shown that a data-driven approach can significantly improve the discriminative power of transfer function analysis (TFA) used to differentiate between normal and impaired cerebral autoregulation (CA) in two groups of data. The data was collected from both healthy subjects (assumed to have normal CA) and symptomatic patients with severe stenosis (assumed to have impaired CA). However, the sample size of the labeled data was relatively small, owing to the difficulty in data collection. Therefore, in this proof-of-concept study, we investigate the feasibility of using an unsupervised learning model to differentiate between normal and impaired CA on TFA variables without requiring labeled data for learning. Approach . Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV), which were recorded simultaneously for approximately 10 min, were included from 148 subjects (41 healthy subjects, 31 with mild stenosis, 13 with moderate stenosis, 22 asymptomatic patients with severe stenosis, and 41 symptomatic patients with severe stenosis). Tiecks' model was used to generate surrogate data with normal and impaired CA. A recently proposed unsupervised learning model was optimized and applied to separate the normal and impaired CA for both the surrogate data and real data. Main results . It achieved 98.9% and 74.1% accuracy for the surrogate and real data, respectively. Significance . To our knowledge, this is the first attempt to employ an unsupervised data-driven approach to assess CA using TFA. This method enables the development of a classifier to determine the status of CA, which is currently lacking., (© 2021 Institute of Physics and Engineering in Medicine.)

Visual inspection of the cervix with acetic acid (VIA), though error prone, has long been used for screening women and to guide management for cervical cancer. The automated visual evaluation (AVE) technique, in which deep learning is used to predict precancer based on a digital image of the acetowhitened cervix, has demonstrated its promise as a low-cost method to improve on human performance. However, there are several challenges in moving AVE beyond proof-of-concept and deploying it as a practical adjunct tool in visual screening. One of them is making AVE robust across images captured using different devices. We propose a new deep learning based clustering approach to investigate whether the images taken by three different devices (a common smartphone, a custom smartphone-based handheld device for cervical imaging, and a clinical colposcope equipped with SLR digital camera-based imaging capability) can be well distinguished from each other with respect to the visual appearance/content within their cervix regions. We argue that disparity in visual appearance of a cervix across devices could be a significant confounding factor in training and generalizing AVE performance. Our method consists of four components: cervix region detection, feature extraction, feature encoding, and clustering. Multiple experiments are conducted to demonstrate the effectiveness of each component and compare alternative methods in each component. Our proposed method achieves high clustering accuracy (97%) and significantly outperforms several representative deep clustering methods on our dataset. The high clustering performance indicates the images taken from these three devices are different with respect to visual appearance. Our results and analysis establish a need for developing a method that minimizes such variance among the images acquired from different devices. It also recognizes the need for large number of training images from different sources for robust device-independent AVE performance worldwide.

Real stressed speech is affected by various aspects (individual characteristics and environment) so that the stress patterns are diverse and different on each individual. To this end, in our previous work, we performed an unsupervised clustering method that able to self-learning manner by mapping the feature representations of the stress speech and clustering tasks simultaneously, called deep time-delay embedded clustering (DTEC). However, DTEC has not confirmed yet the compatibility between the output class and informational classes. Therefore, we proposed semi-supervised time-delay embedded clustering (SDTEC) as a new framework of semi-supervised in DTEC. SDTEC incorporates the prior information of pairwise constraints in the embedding layer and simultaneously learns the feature representation and the clustering assignments. The prior information was used to guide the clustering procedure so that the points that belong to the incorrect cluster can be corrected. The effectiveness of the proposed SDTEC was evaluated by comparing it with some baseline methods in terms of the clustering error rate (CER). Moreover, to demonstrate SDTEC's capabilities, we conducted a comprehensive ablation study. Based on experiment results, SDTEC outperformed the baseline methods and achieves state-of-the-art results in semi-supervised clustering. [ABSTRACT FROM AUTHOR]

Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as music source separation. Contrary to conventional networks that directly estimate the source signals, deep clustering generates an embedding for each time-frequency bin, and separates sources by clustering the bins in the embedding space. We show that deep clustering outperforms conventional networks on a singing voice separation task, in both matched and mismatched conditions, even though conventional networks have the advantage of end-to-end training for best signal approximation, presumably because its more flexible objective engenders better regularization. Since the strengths of deep clustering and conventional network architectures appear complementary, we explore combining them in a single hybrid network trained via an approach akin to multi-task learning. Remarkably, the combination significantly outperforms either of its components.

Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source. Attractor points in this study are created by finding the centroids of the sources in the embedding space, which are subsequently used to determine the similarity of each bin in the mixture to each source. The network is then trained to minimize the reconstruction error of each source by optimizing the embeddings. The proposed model is different from prior works in that it implements an end-to-end training, and it does not depend on the number of sources in the mixture. Two strategies are explored in the test time, K-means and fixed attractor points, where the latter requires no post-processing and can be implemented in real-time. We evaluated our system on Wall Street Journal dataset and show 5.49% improvement over the previous state-of-the-art methods.