nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Mobile Device Synchronization and Remote Control System for High-Performance Medical Applications

The field of telemedicine is in need of generic solutions that harness the power of small, easily carried computing devices to increase efficiency and decrease the likelihood of medical errors. Our study resolved to build a framework to bridge the gap between handheld and desktop solutions by developing an automated network protocol that wirelessly propagates application data and images prepared by a powerful workstation to handheld clients for storage, display and collaborative manipulation. To this end, we present the Mobile Active Medical Protocol (MAMP), a framework capable of nigh-effortlessly linking medical workstation solutions to corresponding control interfaces on handheld devices for remote storage, control and display. The ease-of-use, encapsulation and applicability of this automated solution is designed to provide significant benefits to the rapid development of telemedical solutions. Our results demonstrate that the design of this system allows an acceptable data transfer rate, a usable framerate for diagnostic solutions and enough flexibility to enable its use in a wide variety of cases. To this end, we also present a large-scale multi-modality image viewer as an example application based on the MAMP.
Cross-cohort dementia identification using transfer learning with FDG-PET imaging

Machine learning has recently been applied to the identification of dementia syndromes. Accurately labeled PET brain scans used to train machine learning models, however, are difficult to obtain in the clinical environment. Hence we propose a dementia classification method using transfer learning. The main focus of our research is to train a machine learning model using an accurately labeled source image cohort and an unlabeled target image cohort jointly, and then use this model to label the unlabeled target cohort. We show the effectiveness of this knowledge transfer approach by comparing the proposed method to several other methods on public and private image cohorts.
Annotating Image Regions Using Spatial Context

Image annotation plays an important role in bridging the semantic gap between low level features and high level semantic contents in image access. In this paper, such a task is tackled by annotating regions which are primitives of a visual scene. We propose a probabilistic model to characterize spatial context for region annotation. Such a model provides a unifying framework integrating both feature distribution models and spatial context models. A wide range of advanced modeling techniques can be utilized to further extend this framework. The approach is also potentially scalable to a large number of semantic concepts and a large number of images. Experimental results based on simple parametric models demonstrate promising results of our approach by investigating the impacts of neighbors, segmentation, and visual features
Prostate segmentation in MR images using ensemble deep convolutional neural networks

The automated segmentation of the prostate gland from MR images is increasingly used for clinical diagnosis. Since deep learning demonstrates superior performance in computer vision applications, we propose a coarse-to-fine segmentation strategy using ensemble deep convolutional neural networks (DCNNs) to address prostate segmentation in MR images. First, we use registration-based coarse segmentation on pre-processed prostate MR images to define the potential boundary region. We then train four DCNNs as voxel-based classifiers and classify the voxel in the potential region is a prostate voxel when at least three DCNNs made that decision. Finally, we use boundary refinement to eliminate the outliers and smooth the boundary. We evaluated our approach on the MICCAI PROMIS12 challenge dataset and our experimental results verify the effectiveness of the proposed algorithms.
Optimization and Implementation of H.264 Encoder on DSP Platform

Compared with MPEG-4 and other previous standards, H.264 standard has achieved great breakthrough in coding performance. In this paper, the optimization and implementation of H.264 baseline profile encoder on the TMS320DM642 has been presented. Based on the architectural features of TMS320DM642 and the computational complexity analysis of H.264 encoder, the H.264 encoder has been optimized from three aspects: algorithms, data transfer and memory/Cache use. The experimental results demonstrate that, for the video sequences with CIF format, the optimized H.264 encoder can achieve the encoding speed of more than 24 frames per second, which can meet the real-time requirements of the applications.
A mixed scheme to improve subjective quality in low bitrate video

In wireless multimedia applications, low bitrate video takes an important part. However, its poor subjective quality often dissatisfies consumers. This paper presents a mixed scheme to improve subjective quality in low bitrate video. Our scheme is a combination of several original techniques: fast dynamic key-frame/variable frame-rate coding, ringing effect reduction, band effect reduction, and resolution/quantization tradeoff. The scheme can be applied on many popular video-coding standards, such as MPEG-1, MPEG-2, MPEG-4, and H.264. Additional computing costs involved by the scheme are acceptable. Experiments show significant improvement of subjective quality, and prove that the scheme is valuable.
Content protection and usage control for digital music

The paper presents two fast and robust digital watermarking schemes for compressed music format and MIDI format, which are suitable for online distribution through Internet. For compressed music, the watermark is embedded in partially uncompressed domain and the embedding scheme is high related to music content. For MIDI, the watermark is embedded in virtual notes that are generated and randomly hide among the MIDI notes. The paper also presents a method for controlling usage and protecting against copying of digital music contents and their associated players. The digital music contents and their players installed in one computer cannot be used if they are copied to another computer. Even in the same computer, the duplicated digital music contents are not usable. The usage associated to each music content will restrict the running time and times the music can be played and it will be automatically updated after each playing of the music. The digital music may not be accessible if its permitted usage has expired.
Face recognition using hybrid feature

In this paper, a face recognition algorithm using hybrid feature is proposed. The algorithm consists of three steps. In the first step, a coarse classification is performed using Mutual Information match. In the second step, the Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) features of frequency spectrum are extracted, which will be taken as the input of the RBFN in the third step. In the last step, the classification is carried out by using RBFN. The proposed approach has been tested on ORL face database and Shimon database. The experimental results have demonstrated that the performance of this algorithm is superior to the other algorithms on the same database.
Real-Time Long-Term Tracking With Prediction-Detection-Correction

Real-time long-term visual tracking is one of the most challenging problems in computer vision due to various factors such as occlusion and motion ambiguity. To achieve robust long-term tracking, most state-of-the-art methods typically construct an online detector in each frame. However, they fail to achieve real-time performance due to high computational complexity. In this paper, we propose a novel real-time long-term tracking algorithm by exploiting a joint Prediction-Detection-Correction Tracking framework (PDCT). We utilize a superpixel optical flow to construct a predictor to estimate the target motion and internal scale variation. To locate the target at a finer level, we develop an improved kernelized correlation detector with an adaptive online learning rate and translation-scale parameters from the predictor. To refine the tracking result and redetect the target in the case of a tracking failure, we devise a corrector utilizing dual online SVMs with dense sampling and reliable history samples. The SVMs are trained with passive-aggressive learning and online retraining strategies. In addition, we employ a selection mechanism for the correlation responses to maintain reliable samples effectively. As a result, our proposed tracker is able to refine tracking results via the corrector and detector and maintains reliable tracking results for subsequent tracking. Extensive experiments on the widely used object tracking benchmark show that the proposed tracker is superior to state-of-the-art trackers in terms of both effectiveness and efficiency, and the integration of each component is effective under the PDCT framework.
SparkMed: A Framework for Dynamic Integration of Multimedia Medical Data Into Distributed m-Health Systems

With the advent of 4G and other long-term evolution (LTE) wireless networks, the traditional boundaries of patient record propagation are diminishing as networking technologies extend the reach of hospital infrastructure and provide on-demand mobile access to medical multimedia data. However, due to legacy and proprietary software, storage and decommissioning costs, and the price of centralization and redevelopment, it remains complex, expensive, and often unfeasible for hospitals to deploy their infrastructure for online and mobile use. This paper proposes the SparkMed data integration framework for mobile healthcare (m-Health), which significantly benefits from the enhanced network capabilities of LTE wireless technologies, by enabling a wide range of heterogeneous medical software and database systems (such as the picture archiving and communication systems, hospital information system, and reporting systems) to be dynamically integrated into a cloud-like peer-to-peer multimedia data store. Our framework allows medical data applications to share data with mobile hosts over a wireless network (such as WiFi and 3G), by binding to existing software systems and deploying them as m-Health applications. SparkMed integrates techniques from multimedia streaming, rich Internet applications (RIA), and remote procedure call (RPC) frameworks to construct a Self-managing, Pervasive Automated netwoRK for Medical Enterprise Data (SparkMed). Further, it is resilient to failure, and able to use mobile and handheld devices to maintain its network, even in the absence of dedicated server devices. We have developed a prototype of the SparkMed framework for evaluation on a radiological workflow simulation, which uses SparkMed to deploy a radiological image viewer as an m-Health application for telemedical use by radiologists and stakeholders. We have evaluated our prototype using ten devices over WiFi and 3G, verifying that our framework meets its two main objectives: 1) interactive delivery of medical multimedia data to mobile devices; and 2) attaching to non-networked medical software processes without significantly impacting their performance. Consistent response times of under 500 ms and graphical frame rates of over 5 frames per second were observed under intended usage conditions. Further, overhead measurements displayed linear scalability and low resource requirements.
Weight-based fair intelligent bandwidth allocation for rate adaptive video traffic

We present a weight-based network bandwidth sharing algorithm, fair intelligent bandwidth allocation (FIBA) for transporting rate adaptive video traffic using feedback, and report on its performance. Through simulations the following results are obtained. The algorithm is capable of allocating bandwidth fairly for the MCR plus weighted allocation fairness criteria and MCR plus allocation proportional to PCR fairness criteria, among competitive, rate-adaptive video sources. It is able to reallocate smoothly when there are renegotiations of the minimum guaranteed cell rate, weight, or peak cell rate by some connections, or when a new connection is admitted, or when a connection is throttled somewhere earlier along the connection path. The algorithm prevents congestion, especially during the initial periods when buffer queues can build up significantly.
An anatomically based regionally adaptive prior for MAP reconstruction in emission tomography

The boundary information derived from anatomical images can be incorporated into maximum a posteriori (MAP) reconstruction algorithms to improve the quality of reconstructed images in positron emission tomography (PET). However, challenges arise from mismatches between anatomical (CT) and functional (PET) images which are unavoidable in practice. The aim of this study is to devise a new approach to incorporating anatomical knowledge into emission tomographic reconstruction which is robust to the mismatches while still improving the quality of reconstructed PET images. An anatomically based regionally adaptive regularization MAP (RMAP) is presented. The anatomical knowledge is introduced by labeling the current estimate of the PET image with different anatomical regions derived from the corresponding CT image. An intensity selective non-convex prior is used to model the local smoothness properties adaptively in each anatomical region. The regionally adaptive priors are then combined to form a prior in the Bayesian formulation for the next iteration in the reconstruction. Simulated results show that the proposed algorithm yielded superior lesion contrast recovery, bias- variance tradeoff and robustness to the mismatches between anatomical and functional images compared with MAP with a conventional non-convex prior and MAP with anatomical prior.
Minimum Cross-entropy Reconstruction of PET Images with Anatomically Based Anisotropic Median-Diffusion Filtering

We propose a spatially-variant anisotropic median-diffusion filter prior aided by anatomical knowledge for PET reconstruction. The anisotropic median-diffusion filter is applied locally to an anatomical region which is defined from a co-registered CT image. The individually smoothed regions are then combined to form a prior term in the minimum cross-entropy reconstruction algorithm. A simulated PET thorax phantom with lesions was investigated in terms of bias and contrast versus noise tradeoffs. Compared with MLEM and three other maximum a posteriori (MAP)-like reconstruction algorithms, the proposed algorithm demonstrated better bias-noise tradeoff except when the lesion was close to an anatomical boundary and better contrast-noise tradeoff in all cases.
A supervised multiview spectral embedding method for neuroimaging classification

The multi-view/multi-modal features are commonly used in neuroimaging classification because they could provide complementary information to each other and thus result in better classification performance than single-view features. However, it is very challenging to effectively integrate such rich features, since straightforward concatenation or singleview spectral embedding methods rarely leads to physically meaningful integration. In this paper, we present a supervised multi-view/multi-modal spectral embedding method (SMSE) for neuroimaging classification. This method embeds the high dimensional multi-view features derived from multi-modal neuroimaging data into a low dimensional feature space and preserves the optimal local embeddings among different views. The proposed SMSE algorithm, validated using three groups of neuroimaging data, is able to achieve significant classification improvement over the state-of-the-art multi-view spectral embedding methods.
A Study on Static Image Derived Input Function for Non-invasively Constructing Parametric Image in Functional Imaging

Positron emission tomography (PET), as functional imaging, provides in-vivo spatial distribution of physiological or biochemical processes. The kinetic modelling process to derive quantitative functional parameter usually requires invasive frequent blood sampling. We proposed a new approach to use static imaging derived information to produce non-invasive input function (SID-IF). The performance of SID-IF was investigated by 609 clinical neurological studies in non-invasively constructing parametric images of cerebral metabolic rate of glucose consumption (CMRGlc). The performance of the personal information feature based input function method (PIFB-IF) was also evaluated in the investigation. The results of area under curve and CMRGlc demonstrated the image feature derived by cerebellum provided less bias in the estimation of SID-IF. The performance of SID-IF was sensitive to the choice of the training set and the plasma glucose concentration in the data set may improve the estimated accuracy. The PIFB-IF method was less sensitive to the glucose range and choice of training set.
Concept Constrained Image Region Annotation

Annotating image regions has been a challenging open issue in many areas such as image content understanding and image retrieval. In this paper, rather than solely rely on visual features of image regions, a novel approach is proposed to improve region annotation by taking concept constraints into account, since high level conceptual information such as image categories can increase the confidence of possible region labels as well as decrease the confidence of impossible region labels. We employ statistical models to learn the relationships among visual features, image concepts, and region labels. As a result, a set of possible region labels can be derived from a set of visual feature vectors of a given image so as to refine the annotation output obtained by using visual feature only. Promising experimental results have been demonstrated on 8462 regions of the University of Washington image dataset with diverse concepts for the proposed approach.
Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information

Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.
Cellular automata and anisotropic diffusion filter based interactive tumor segmentation for positron emission tomography

Tumor segmentation in positron emission tomography (PET) aids clinical diagnosis and in assessing treatment response. However, the low resolution and signal-to-noise inherent in PET images, makes accurate tumor segmentation challenging. Manual delineation is time-consuming and subjective, whereas fully automated algorithms are often limited to particular tumor types, and have difficulties in segmenting small and low-contrast tumors. Interactive segmentation may reduce the inter-observer variability and minimize the user input. In this study, we present a new interactive PET tumor segmentation method based on cellular automata (CA) and a nonlinear anisotropic diffusion filter (ADF). CA is tolerant of noise and image pattern complexity while ADF reduces noise while preserving edges. By coupling CA with ADF, our proposed approach was robust and accurate in detecting and segmenting noisy tumors. We evaluated our method with computer simulation and clinical data and it outperformed other common interactive PET segmentation algorithms.
Large Margin Local Estimate With Applications to Medical Image Classification

Medical images usually exhibit large intra-class variation and inter-class ambiguity in the feature space, which could affect classification accuracy. To tackle this issue, we propose a new Large Margin Local Estimate (LMLE) classification model with sub-categorization based sparse representation. We first sub-categorize the reference sets of different classes into multiple clusters, to reduce feature variation within each subcategory compared to the entire reference set. Local estimates are generated for the test image using sparse representation with reference subcategories as the dictionaries. The similarity between the test image and each class is then computed by fusing the distances with the local estimates in a learning-based large margin aggregation construct to alleviate the problem of inter-class ambiguity. The derived similarities are finally used to determine the class label. We demonstrate that our LMLE model is generally applicable to different imaging modalities, and applied it to three tasks: interstitial lung disease (ILD) classification on high-resolution computed tomography (HRCT) images, phenotype binary classification and continuous regression on brain magnetic resonance (MR) imaging. Our experimental results show statistically significant performance improvements over existing popular classifiers.
Automated segmentation of tumour changes in temporal PET-CT data

Positron emission tomography and computed tomography (PET-CT) is widely accepted as the best imaging in the management of cancer. To assess response to treatment, multiple scans of a patient are usually acquired and tumour changes are analysed at different stages during the treatment. However, it is difficult to assimilate these changes, especially where a patient has in excess of e.g. four temporal scans. We aimed to advance current single-time PET tumour segmentation algorithms to multiple-time PET studies. We therefore proposed a new treatment response confidence connected region growing (TRCCRG) algorithm. Our algorithm is interactive and includes a combined function of Radial profile plot and Rodbard curve fitting as a measure of tumour likelihood for the morphologically expanded volume in successive scans. Evaluation with 10 simulated PET data and 6 clinical lymphoma PET-CT patients (each with 4 studies) demonstrated that the proposed method was successful in detecting and delineating tumours.
The impact of reconstruction algorithms on semi-automatic small lesion segmentation for PET: A phantom study

A robust lesion segmentation method is critical for quantification of lesion activity in positron emission tomography (PET), especially for the cases where lesion boundary is not discernible in the corresponding computed tomography (CT). However, lesion delineation in PET is a challenging task, especially for small lesions, due to the low intrinsic resolution, image noise and partial volume effect. The combinations of different reconstruction methods and post-reconstruction smoothing on PET images also affect the segmentation result significantly which has always been overlooked. Therefore, the aim of this study was to investigate the impact of different reconstruction methods on semi-automated small lesion segmentation for PET images. Four conventional segmentation methods were evaluated including region growing technique based on maximum intensity (RGmax) and mean intensity (RGmean) thresholds, Fuzzy c-mean (FCM) and watershed (WS) technique. All these methods were evaluated on a physical phantom scan which was reconstructed with Ordered Subset Expectation Maximization (OSEM) with Gaussian post-smoothing and Maximum a Posteriori (MAP) with quadratic prior respectively. The results demonstrate that: 1) the performance of all the segmentation methods subject to the smoothness constraint applied on the reconstructed images; 2) FCM method applied on MAP reconstructed images yielded overall superior performance than other evaluated combinations.