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Wireless capsule endoscopy is a non-invasive and painless procedure to detect anomalies from the gastrointestinal tract. Single examination results in up to 8 hrs of video and requires between 45 - 180 mins for diagnosis depending on the complexity. Image and video computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, a compact U-Net with lesser encoder-decoder pairs is presented, to detect and precisely segment bleeding and red lesions from endoscopy data. The proposed compact U-Net is compared with the original U-Net and also with other methods reported in the literature. The results show the proposed compact network performs on par with the original network but with faster training and lesser memory consumption. Also, the proposed model provided a dice score of 91% outperforming other methods reported on a blind tested WCE dataset with no images from this set used for training.Epilepsy is a neurological disorder that causes sudden seizures due to abnormal excitation of neurons in the brain. Approximately 30 % of patients cannot control their seizures using medication. In addition, since seizures can occur anywhere and at any time, caregivers must always be with the patient. Various researchers have developed seizure detection methods using multichannel EEG to improve the quality of life of patients and caregivers. However, the large size of the measurement device impedes transportation. We believe that a portable measurement device with a small number of channels is suitable for detecting seizures in daily life. Therefore, we need a system that can detect seizures using a small number of channels. The purpose of this research is to develop a seizure detection algorithm using a single-channel frontal EEG and to confirm its basic performance. We used EEG signals from a single electrode position (Fp1-F7, Fp2-F8), which is a bipolar derivation of the frontal region. We segmented the EEG using a 2 s sliding window with 50 % overlap and converted the segments into images. After preprocessing, we fine-tuned ResNet18, pre-trained on ImageNet, and developed an ensemble classification method. In the experiments with 10 epileptic patients (3 - 19 years old) registered in the CHB-MIT scalp EEG database, the results showed that the average sensitivity was 88.73 %, the average specificity was 98.98 %, and the average detection latency time was 7.39 s. In conclusion, the developed algorithm was validated as sufficiently accurate to detect epileptic seizures.Clinical Relevance- This establishes an image recognition algorithm that can detect epileptic seizures using a single- channel frontal EEG.Automatic segmentation of the kidney and tumor from computed tomography (CT) images is an essential step in precision oncology and personalized treatment planning. Due to the irregular shapes and vague boundaries of kidney and tumor, this is a challenging task. Most of existing methods focused on local features without fully considering the associations between regions and contextual relationships between features. We propose a new segmentation method, CR-UNet, to extract, encode and adaptively integrate multiple layers of relevant features. Since the semantic features of different channels contribute differently to the segmentation of kidney and tumor, we introduce semantic attention mechanism of channels. The regional association attention mechanism is established to integrate the semantic and positional connections between different regions. Ablation studies demonstrate the contributions of semantic associations between deep learning channels, and regional relation modelling. Comparison results with state-of-the-art methods over public dataset demonstrated improved tumor and kidney segmentation performance.Melanoma is considered as one of the world's deadly cancers. This type of skin cancer will spread to other areas of the body if not detected at an early stage. Convolutional Neural Network (CNN) based classifiers are currently considered one of the most effective melanoma detection techniques. This study presents the use of recent deep CNN approaches to detect melanoma skin cancer and investigate suspicious lesions. Tests were conducted using a set of more than 36,000 images extracted from multiple datasets. The obtained results show that the best performing deep learning approach achieves high scores with an accuracy and Area Under Curve (AUC) above 99%.During endoscopic surgery, smoke removal is important and meaningful for increasing the visual quality of endoscopic images. However, unlike natural image dehaze, it is practical impossible to build a large paired endoscopic image training dataset with/without smoke. Therefore, in this paper, we propose a new approach, called Desmoke-CycleGAN, which combined detection and removal of smoke together, to improve the CycleGAN model for endoscopic image smoke removal. The detector can provide information about smoke locations and densities, which helps the GAN model to be more stable and efficient for smoke removal. Although some imperfections still exist, the experimental results have demonstrated that this method outperforms other state-of-the-art smoke removal approaches with unpaired real endoscopic images.Clinical Relevance- This can help improve the visibility in endoscopic surgery and to get smoke-free endoscopic images with better quality.The study of brain network connectivity as a time-varying property began relatively recently and to date has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group- level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a planar embedding of the high-dimensional whole-brain connectivity dynamics that preserves important features, such as trajectory continuity, characterizing dynamics in the native high dimensional state space. The method is validated in application to a large rs- fMRI study of schizophrenia where it extracts naturalistic fluidly-varying connectivity motifs that differ between schizophrenia patients (SZs) and healthy controls (HC).Functional Magnetic Resonance Imaging, Functional Network Connectivity, Dynamic Functional Network Connectivity, Schizophrenia.Instrument segmentation is a crucial and challenging task for robot-assisted surgery operations. Recent commonly-used models extract feature maps in multiple scales and combine them via simple but inferior feature fusion strategies. In this paper, we propose a hierarchical attentional feature fusion scheme, which is efficient and compatible with encoder-decoder architectures. Specifically, to better combine feature maps between adjacent scales, we introduce dense pixel-wise relative attentions learned from the segmentation model; to resolve specific failure modes in predicted masks, we integrate the above attentional feature fusion strategy based on position-channel-aware parallel attention into the decoder. Extensive experimental results evaluated on three datasets from MICCAI 2017 EndoVis Challenge demonstrate that our model outperforms other state-of-the-art counterparts by a large margin.Parallel magnetic resonance imaging (pMRI) accelerates data acquisition by undersampling k-space through an array of receiver coils. Finding accurate relationships between acquired and missing k-space data determines the interpolation performance and reconstruction quality. Autocalibration signals (ACS) are generally used to learn the interpolation coefficients for reconstructing the missing k-space data. Based on the estimation-approximation error analysis in machine learning, increasing training data size can reduce estimation error and therefore enhance generalization ability of the interpolator, but scanning time will be longer if more ACS data are acquired. We propose to augment training data using unacquired and acquired data outside of ACS region through semi-supervised learning idea and autoregressive model. Local neighbor unacquired k-space data can be used for training tasks and reducing the generalization error. Experimental results show that the proposed method outperforms the conventional methods by suppressing noise and aliasing artifacts.CT machines can be tuned in order to reduce the radiation dose used for imaging, yet reducing the radiation dose results in noisy images which are not suitable in clinical practice. In order for low dose CT to be used effectively in practice this issue must be addressed. Generative Adversarial Networks (GAN) have been used widely in computer vision research and have proven themselves as a powerful tool for producing images with high perceptual quality. In this work we use a cascade of two neural networks, the first is a Generative Adversarial Network and the second is a Deep Convolutional Neural Network. The first network generates a denoised sample which is then fine-tuned by the second network via residue learning. We show that our cascaded method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.Molecular imaging has long been recognized as an important tool for diagnosis, characterization, and monitoring of treatment responses of brain tumors. Magnetic resonance spectroscopic imaging (MRSI) is a label-free molecular imaging technique capable of mapping metabolite distributions non-invasively. Several metabolites detectable by MRSI, including Choline, Lactate and N-Acetyl Aspartate, have been proved useful biomarkers for brain tumor characterization. However, clinical application of MRSI has been limited by poor resolution, small spatial coverage, low signal-to-noise ratio and long scan time. This work presents a novel MRSI method for fast, high-resolution metabolic imaging of brain tumor. This method synergistically integrates fast acquisition sequence, sparse sampling, subspace modeling and machine learning to enable 3D mapping of brain metabolites with a spatial resolution of 2.0×3.0×3.0 mm3 in a 7-minute scan. Experimental results obtained from patients with diagnosed brain tumor showed great promise for capturing small-size tumors and revealing intra-tumor metabolic heterogeneities.Clinical Relevance - This paper presents a novel technique for label-free molecular imaging of brain tumor. With further development, this technology may enable many potential clinical applications, from tumor detection, characterization, to assessment of treatment efficacy.Joint effusion is a hallmark of osteoarthritis (OA) associated with stiffness, and may relate to pain, disability, and long-term outcomes. However, it is difficult to quantify accurately. We propose a new Deep Learning (DL) approach for automatic effusion assessment from Magnetic Resonance Imaging (MRI) using volumetric quantification measures (VQM). We developed a new multiplane ensemble convolutional neural network (CNN) approach for 1) localizing bony anatomy and 2) detecting effusion regions. CNNs were trained on femoral head and effusion regions manually segmented from 3856 images (63 patients). Upon validation on a non-overlapping set of 2040 images (34 patients) DL showed high agreement with ground-truth in terms of Dice score (0.85), sensitivity (0.86) and precision (0.83). Agreement of VQM per-patient was high for DL vs experts in term of Intraclass correlation coefficient (ICC)= 0.88[0.80,0.93]. Zn-C3 inhibitor We expect this technique to reduce inter-observer variability in effusion assessment, reducing expert time and potentially improving the quality of OA care.
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