varunr14/newsummary huggingface dataset:
Background Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis facial surgery planning prosthesis design and forensic identification . Objectives In this study a Bayesian method for segmentation of facial tissues is presented . Segmentation classes include muscle bone fat air and skin . Methods The method presented incorporates information fusion from multiple modalities modelling of image resolution image noise two priors helping to reduce noise and partial volume . Image resolution modelling employed facilitates resolution enhancement and superresolution capabilities during image segmentation . Regularization based on isotropic and directional Markov Random Field priors is integrated . The Bayesian model is solved iteratively yielding tissue class labels at every voxel of the image . Sub methods as variations of the main method are generated by using a combination of the models . Results Testing of the sub methods is performed on two patients using single modality three dimensional image as well as registered MR CT images with information fusion . Numerical visual and statistical analyses of the methods are conducted . High segmentation accuracy values are obtained by the use of image resolution and partial volume models as well as information fusion from MR and CT images . The methods are also compared with our Bayesian segmentation method proposed in a previous study . The performance is found to be similar to our previous Bayesian approach but the presented methods here eliminates ad hoc parameter tuning needed by the previous approach which is system and data acquisition setting dependent . Conclusions The Bayesian approach presented provides resolution enhanced segmentation of very thin structures of the human head . Meanwhile free parameters of the algorithm can be adjusted for different imaging systems and data acquisition settings in a more systematic way as compared with our previous study . 


Summary: Bayesian method provides resolution enhanced segmentation of human head. Segmentation classes incllude muscle bone fat air and skin. Tests were performed on 3D MR and CT images as well as registered MR CT images. The most successful results were obtained by the information fusion of MR and CT. Free parameters of the algorithm can be adjusted in a more systematic way.
The assessment of microcirculation spatial heterogeneity on the hand skin is the main objective of this work . Near infrared spectroscopy based 2D imaging is a non invasive technique for the assessment of tissue oxygenation . The haemoglobin oxygen saturation images were acquired by a dedicated camera during baseline ischaemia and reperfusion . Acquired images underwent a preliminary restoration process aimed at removing degradations occurring during signal capturing . Then wavelet transform based multiscale analysis was applied to identify edges by detecting local maxima and minima across successive scales . Segmentation of test areas during different conditions was obtained by thresholding based region growing approach . The method identifies the differences in microcirculatory control of blood flow in different regions of the hand skin . The obtained results demonstrate the potential use of NIRS images for the clinical evaluation of skin disease and microcirculatory dysfunction . 


Summary: We study oxygen saturation by vascular occlusion test. NIRS camera captured 2D images of hand during ischaemia and in perfusion. We applied edge detection and segmentation to NIRS images. With the applied methods we can visualize the oxygenation changes. The method allows to quantitate and visualize oxygenation at the same time.
Background and objective We live our lives by the calendar and the clock but time is also an abstraction even an illusion . The sense of time can be both domain specific and complex and is often left implicit requiring significant domain knowledge to accurately recognize and harness . In the clinical domain the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time . Electronic health records have paved the way to making these data available for practitioners and researchers . However temporal data representation normalization extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline . The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state of the art in processing temporal information of clinical narratives . Methods This review surveys the methods used in three important area modeling and representing of time medical NLP methods for extracting time and methods of time reasoning and processing . The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations . Results The main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations . Conclusions Extracting temporal information in clinical narratives is a challenging task . The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and together with medical NLP techniques will help resolving granularity and co reference resolution problems . 


Summary: Multifaceted aspects in time and time oriented concepts. Comparison of clinical data models in handling time. Ontologies of representation and reasoning about time in the clinical domain. Constructing the timelines for the medical histories of patients. Temporal concept coreference resolution problem.
In crisis situations a seamless ubiquitous communication is necessary to provide emergency medical service to save people s lives . An excellent prehospital emergency medicine provides immediate medical care to increase the survival rate of patients . On their way to the hospital ambulance personnel must transmit real time and uninterrupted patient information to the hospital to apprise the physician of the situation and provide options to the ambulance personnel . In emergency and crisis situations many communication channels can be unserviceable because of damage to equipment or loss of power . Thus data transmission over wireless communication to achieve uninterrupted network services is a major obstacle . This study proposes a mobile middleware for cognitive radio for improving the wireless communication link . CRs can sense their operating environment and optimize the spectrum usage so that the mobile middleware can integrate the existing wireless communication systems with a seamless communication service in heterogeneous network environments . Eventually the proposed seamless mobile communication middleware was ported into an embedded system which is compatible with the actual network environment without the need for changing the original system architecture . 


Summary: In emergency and crisis situations many communication channels can be unserviceable because of damage to equipment or loss of power. Thus data transmission over wireless communication to achieve uninterrupted network services is a major obstacle. The proposed middleware can sense its operating environment and optimize the spectrum usage to integrate the existing heterogeneous wireless communication systems. The proposed middleware was ported into an embedded system which is compatible with the actual network environment without the need for changing the original system architecture.
Cataract is defined as a lenticular opacity presenting usually with poor visual acuity . It is one of the most common causes of visual impairment worldwide . Early diagnosis demands the expertise of trained healthcare professionals which may present a barrier to early intervention due to underlying costs . To date studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity . We present an ensemble learning based approach as a means to improving diagnostic accuracy . Three independent feature sets i.e . wavelet sketch and texture based features are extracted from each fundus image . For each feature set two base learning models i.e . Support Vector Machine and Back Propagation Neural Network are built . Then the ensemble methods majority voting and stacking are investigated to combine the multiple base learning models for final fundus image classification . Empirical experiments are conducted for cataract detection and cataract grading tasks . The best performance of the ensemble classifier is 93.2 and 84.5 in terms of the correct classification rates for cataract detection and grading tasks respectively . The results demonstrate that the ensemble classifier outperforms the single learning model significantly which also illustrates the effectiveness of the proposed approach . 


Summary: Fundus image analysis provides great potentials for automatic cataract diagnosis. Ensemble learning is exploited for combining multiple models to improve the performance. Three independent feature sets i.e. wavelet sketch and texture based features are utilized. Two base learning models i.e. Support Vector Machine and Back Propagation Neural Network are investigated in the ensemble approach. The experiments demonstrate that our approach outperforms single model significantly.
Background and objective The automatic classification of breast imaging lesions is currently an unsolved problem . This paper describes an innovative representation learning framework for breast cancer diagnosis in mammography that integrates deep learning techniques to automatically learn discriminative features avoiding the design of specific hand crafted image based feature detectors . Methods A new biopsy proven benchmarking dataset was built from 344 breast cancer patients cases containing a total of 736 film mammography views representative of manually segmented lesions associated with masses 426 benign lesions and 310 malignant lesions . The developed method comprises two main stages preprocessing to enhance image details and supervised training for learning both the features and the breast imaging lesions classifier . In contrast to previous works we adopt a hybrid approach where convolutional neural networks are used to learn the representation in a supervised way instead of designing particular descriptors to explain the content of mammography images . Results Experimental results using the developed benchmarking breast cancer dataset demonstrated that our method exhibits significant improved performance when compared to state of the art image descriptors such as histogram of oriented gradients and histogram of the gradient divergence increasing the performance from 0.787 to 0.822 in terms of the area under the ROC curve . Interestingly this model also outperforms a set of hand crafted features that take advantage of additional information from segmentation by the radiologist . Finally the combination of both representations learned and hand crafted resulted in the best descriptor for mass lesion classification obtaining 0.826 in the AUC score . Conclusions A novel deep learning based framework to automatically address classification of breast mass lesions in mammography was developed . 


Summary: Innovative representation learning framework for breast cancer lesion classification. A hybrid CNN method to learn image based features in a supervised way. New breast cancer benchmarking dataset to support computer aided diagnosis methods.
Nowadays the diagnosis and treatment of pelvic sarcoma pose a major surgical challenge for reconstruction in orthopedics . With the development of manufacturing technology the metal 3D printed customized implants have brought revolution for the limb salvage resection and reconstruction surgery . However the tumor resection is not without risk and the precise implant placement is very difficult due to the anatomic intricacies of the pelvis . In this study a surgical navigation system including the implant calibration algorithm has been developed so that the surgical instruments and the 3D printed customized implant can be tracked and rendered on the computer screen in real time minimizing the risks and improving the precision of the surgery . Both the phantom experiment and the pilot clinical case study presented the feasibility of our computer aided surgical navigation system . According to the accuracy evaluation experiment the precision of customized implant installation can be improved three to five times compared with the non navigated implant installation after the guided osteotomy which means it is sufficient to meet the clinical requirements of the pelvic reconstruction . However more clinical trials will be conducted in the future work for the validation of the reliability and efficiency of our navigation system . 


Summary: A navigation system was developed for pelvic tumor resection and reconstruction. A novel method for tracking the 3D printed patient specific implant was proposed. Both the phantom and clinical case study demonstrated the system feasibility. The high and stable accuracy regarding TRE demonstrated the system repeatability.
Iterative proportional fitting is a widely used method for spatial microsimulation . The technique results in non integer weights for individual rows of data . This is problematic for certain applications and has led many researchers to favour combinatorial optimisation approaches such as simulated annealing . An alternative to this is integerisation of IPF weights the translation of the continuous weight variable into a discrete number of unique or cloned individuals . We describe four existing methods of integerisation and present a new one . Our method truncate replicate sample recognises that IPF weights consist of both replication weights and conventional weights the effects of which need to be separated . The procedure consists of three steps separate replication and conventional weights by truncation replication of individuals with positive integer weights and probabilistic sampling . The results which are reproducible using supplementary code and data published alongside this paper show that TRS is fast and more accurate than alternative approaches to integerisation . 


Summary: Integerisation of weights obtained from iterative proportional fitting. Evaluation of five methods for integerisation. New method presented based on truncation replication and sampling TRS . The new method outperforms previously published integerisation strategies. Easily reproducible results using publicly available R code and data.
Background and objectives Computer aided analysis of mammograms has been employed by radiologists as a vital tool to increase the precision in the diagnosis of breast cancer . The efficiency of such an analysis is dependent on the employed mammogram enhancement approach as its major role is to yield a visually improved image for radiologists . Methods Non linear Polynomial Filtering framework has been explored previously as a robust approach for contrast improvement of mammographic images . This paper presents the extension of NPF framework for sharpening and edge enhancement of mammogram lesions . Proposed NPF serves to provide enhancement of edges and sharpness of the lesion region in mammograms in a manner to minimize the dependencies on pre selected thresholds . In the present work Logarithmic Image Processing model has been employed for the purpose of improvement in visualization of mammograms based on Human Visual System characteristics . Results The proposed NPF filtering framework yields mammograms with significant improvement in contrast edges as well as sharpness of the lesion region . The performance of the proposed approach has been validated using state of art objective evaluation measures like Contrast Improvement Index Peak Signal to Noise Ratio Average Signal to Noise Ratio and Combined Enhancement Measure as well as subjective evaluation by radiologists opinions . Conclusions The proposed NPF provides a robust solution to perform noise controlled contrast as well as edge enhancement using a single filtering model . This leads to a better visualization of the fine lesion details predictive of their severity . The applicability of single filtering methodology for carrying out denoising contrast and edge enhancement improves the worth of the overall framework . 


Summary: NPF framework serves to catalyze the computer aided analysis of mammograms. It provides the radiologists with improved mammograms for precision in diagnosis. An extension of NPF for sharpening and edge enhancement of mammograms is presented. Integration of LIP model provides enhancement response in coherence to HVS. The obtained results are evaluated using CII PSNR CEM as image quality metrics.
The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena . In this context the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days . Among the advantages resulting from using these models is their application to optimization and inverse problem approaches . Traditionally the formulated Optimal Control Problem has the objective of minimizing the size of tumor cells by the end of the treatment . In this case an important aspect is not considered namely the optimal concentrations of drugs may affect the patients health significantly . In this sense the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer through the minimization of both the cancerous cells concentration and the prescribed drug concentration . The resolution of this multi objective problem is obtained through the Multi objective Optimization Differential Evolution algorithm . The Pareto s Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen according to a given criterion . 


Summary: To formulate and to solve a multi objective optimal control problem applied to treatment of tumors. To minimize the cancerous cells concentration and the drug concentration. The results demonstrated that an optimal protocol can be obtained considering both objectives.
To facilitate the performance comparison of new methods for sleep patterns analysis datasets with quality content publicly available are very important and useful . We introduce an open access comprehensive sleep dataset called ISRUC Sleep . The data were obtained from human adults including healthy subjects subjects with sleep disorders and subjects under the effect of sleep medication . Each recording was randomly selected between PSG recordings that were acquired by the Sleep Medicine Centre of the Hospital of Coimbra University . The dataset comprises three groups of data data concerning 100 subjects with one recording session per subject data gathered from 8 subjects two recording sessions were performed per subject and data collected from one recording session related to 10 healthy subjects . The polysomnography recordings associated with each subject were visually scored by two human experts . Comparing the existing sleep related public datasets ISRUC Sleep provides data of a reasonable number of subjects with different characteristics such as data useful for studies involving changes in the PSG signals over time and data of healthy subjects useful for studies involving comparison of healthy subjects with the patients suffering from sleep disorders . This dataset was created aiming to complement existing datasets by providing easy to apply data collection with some characteristics not covered yet . ISRUC Sleep can be useful for analysis of new contributions in biomedical signal processing in development of ASSC methods and on sleep physiology studies . To evaluate and compare new contributions which use this dataset as a benchmark results of applying a subject independent automatic sleep stage classification method on ISRUC Sleep dataset are presented . 


Summary: A publicly available sleep dataset called ISRUC Sleep is introduced. It contains data of healthy and patient subjects with different characteristics. Two recording sessions per subject are included in subgroup II of the dataset. ISRUC Sleep were evaluated using the ASSC method SSM4S. Effects of sleep disorders diseases and medications are presented.
Dictionary learning plays a crucial role in sparse representation based image classification . In this paper we propose a novel approach to learn a discriminative dictionary with low rank regularization on the dictionary . Specifically we apply Fisher discriminant function to the coding coefficients to make the dictionary more discerning that is a small ratio of the within class scatter to between class scatter . In practice noisy information in the training samples will undermine the discriminative ability of the dictionary . Inspired by the recent advances in low rank matrix recovery theory we apply low rank regularization on the dictionary to tackle this problem . The iterative projection method and inexact augmented Lagrange multiplier algorithm are adopted to solve our objective function . The proposed discriminative dictionary learning with low rank regularization approach is evaluated on four face and digit image datasets in comparison with existing representative dictionary learning and classification algorithms . The experimental results demonstrate the superiority of our approach . 


Summary: Learn a discriminative dictionary with low rank regularization Fisher discriminant function is applied to the coding coefficients. IPM and ALM algorithms are adopted to solve our objective function.
Background and objective Cosmetic outcome of breast cancer conservative treatment remains without a standard evaluation method . Subjective methods in spite of their low reproducibility continue to be the most frequently used . Objective methods although more reproducible seem unable to translate all the subtleties involved in cosmetic outcome . The breast cancer conservative treatment cosmetic results software was developed in 2007 to try to overcome these pitfalls . The software is a semi automatic objective tool that evaluates asymmetry color differences and scar visibility using patient s digital pictures . The purpose of this work is to review the use of the BCCT.core software since its availability in 2007 and to put forward future developments . Methods All the online requests for BCCT.core use were registered from June 2007 to December 2014 . For each request the department city and country as well as user intention were questioned . A literature search was performed in Medline Google Scholar and ISI Web of Knowledge for all publications using and citing BCCT.core . Results During this period 102 centers have requested the software essentially for clinical use . The BCCT.core software was used in 19 full published papers and in 29 conference abstracts . Conclusions The BCCT.core is a user friendly semi automatic method for the objective evaluation of BCCT . The number of online requests and publications have been steadily increasing turning this computer program into the most frequently used tool for the objective cosmetic evaluation of BCCT . 


Summary: The BCCT.core is a computer program used for the esthetic evaluation of breast cancer conservative treatment. The BCCT.core software has been used by centres worldwide for clinical use and research. Several publications have confirmed its easiness of use and reproduciblity.