Encoder Enhanced Atrous (EEA) Unet architecture for Retinal Blood vessel segmentation

The retinal blood vessel segmentation is required for continuously monitoring the blood vessel in most of the retinal disease diagnosis. Deep learning approaches are accepted as the promising techniques for biomedical image segmentation. In this paper, Encoder enhanced Atrous architecture is proposed for retinal blood vessel segmentation. The encoder section is enhanced by improving the depth concatenation process with the addition layers. The proposed architecture is evaluated on the publicly available databases DRIVE, STARE, CHASE_DB1 and HRF using metrics like accuracy, sensitivity, specificity, Dice coefficient, and Mathew’s correlation coefficient. The proposed architecture performs better compared to the conventional Unet architecture in terms of accuracy by 0.35% and 0.83% for DRIVE and STARE respectively. In terms of specificity and Dice score, the proposed architecture also shows improved results compared to the Unet architecture.     

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.     

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

ObjectivesLiver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method.MethodsIn this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET).ResultsWe have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process.ConclusionsOur developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.     

Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images

Alzheimer’s disease, the most common form of dementia is a neurodegenerative brain order that has currently no cure for it. Hence, early diagnosis of such disease using computer-aided systems is a subject of great importance and extensive research amongst researchers. Nowadays, deep learning or particularly convolutional neural network (CNN) is getting more attention due to its state-of-the-art performances in variety of computer vision tasks such as visual object classification, detection and segmentation. Several recent studies, that have used brain MRI scans and deep learning have shown promising results for diagnosis of Alzheimer’s disease. However, most common issue with deep learning architectures such as CNN is that they require large amount of data for training. In this paper, a mathematical model P_FS_EC_TL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task. Experimentation is performed on data collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The accuracy of the 3-way classification using the described method is 95.73% for the validation set.     

分段是视频学习的有效方式吗?

教育心理学中的分段原则认为,将视频学习材料分割成几个小片段进行学习的效果更好。以往关于分段的研究通过两个角度的操纵考察了分段在学习中的作用,一方面是交互性角度,学习者自己控制的分段及系统分段;另一方面是结构性角度,分段是在任意时间点划分和有意义时间点上划分。通过对以往研究结果的整理发现,大部分研究证明分段学习相较于连续学习的成绩更好,并且能降低学习者在学习过程中的感知难度, 提高积极的情感状态,但是对心理努力的影响比较微弱。研究者们从事件分割理论和认知负荷理论的视角对分段的效果做出了解释。未来研究需要继续关注分段的边界条件、拓宽研究的范围、探究分段的认知神经基础等不断完善分段原则在教学中的应用。     

Wild facial expression recognition based on incremental active learning

Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches.     

The continued search for nonarbitrary metrics in psychology

H. Blanton and J. Jaccard examined the arbitrariness of metrics in the context of 2 current issues: (a) the measurement of racial prejudice and (b) the establishment of clinically significant change. According to Blanton and Jaccard, although research findings are not undermined by arbitrary metrics, individual scores and score changes may not be meaningfully interpreted. The author believes that their points are mostly valid and that their examples were appropriate. However, Blanton and Jaccard's article does not lead directly to solutions, nor did it adequately describe the scope of the metric problem. This article has 2 major goals. First, some prerequisites for nonarbitrary metrics are presented and related to Blanton and Jaccard's issues. Second, the impact of arbitrary metrics on psychological research findings are described. In contrast to Blanton and Jaccard (2006), research findings suggest that metrics have direct impact on statistics for group comparisons and trend analysis.     

ECNN: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder

This paper aims to apply deep learning to identify autism spectrum disorder (ASD) patients from a large brain imaging dataset based on the patients’ brain activation patterns. The brain images are collected from the ABIDE (Autism Brain Imaging Data Exchange) database. The proposed convolutional neural network (CNN) architecture investigates functional connectivity patterns between different brain areas to identify specifics patterns to diagnose ASD. The enhanced CNN uses blocks of temporal convolutional layers that employ casual convolutions and dilations; hence, it is suitable for sequential data with temporality large receptive fields. Experimental results show that the proposed ECNN achieves an accuracy of up to 80% accuracy. These patterns show an anticorrelation of brain function between anterior and posterior areas of the brain; that is, the disruption in brain connectivity is one primary evidence of ASD.     

Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI

Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In this research, deep learning model is presented for glioma and stroke lesion detection. The proposed architecture consists of 14 layers. The first input layer is followed by three convolutional layers while 5th, 6th and 7th layers correspond to batch normalization, followed by next three layers of rectified linear unit (ReLU). Eleventh layer is average pooling 2D which is followed by fully connected (FC), softmax and classification layers respectively. The presented method is verified on six MICCAI databases namely multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016, 2017 and sub-acute ischemic stroke lesion segmentation (SISS-ISLES) 2015. The computational time is also measured across each benchmark dataset such as 53 s on BRATS 2013, 26 s on BRATS 2014, 41 s on BRATS 2015, 36 s on BRATS 2016, and 38 s on BRATS 2017 and 4.13 s on ISLES 2015 proving that the proposed technique has less processing time. The proposed method achieved 0.9943 ACC, 1.00 SP, 0.9839 SE on BRATS 2013, 0.9538 ACC, 0.9991 SP, 0.7196 SE on BRATS 2014, 0.9978 ACC, 1.00 SP, 0.9919 SE on BRATS 2015, 0.9569 ACC, 0.9491 SP, 0.9755 SE on BRAST 2016, 0.9778 ACC, 0.9770 SP, 0.9789 SE on BRATS 2017 and 0.9227 ACC, 1.00 SP, 0.8814 SP on ISLES 2015 datasets respectively.     

EVENT SEGMENTATION

ABSTRACT— One way to understand something is to break it up into parts. New research indicates that segmenting ongoing activity into meaningful events is a core component of perception and that this has consequences for memory and learning. Behavioral and neuroimaging data suggest that event segmentation is automatic and that people spontaneously segment activity into hierarchically organized parts and subparts. This segmentation depends on the bottom-up processing of sensory features such as movement and on the top-down processing of conceptual features such as actors' goals. How people segment activity affects what they remember later; as a result, those who identify appropriate event boundaries during perception tend to remember more and to learn more proficiently.     

The effect of sonority on word segmentation: evidence for the use of a phonological universal

It has been well documented how language-specific cues may be used for word segmentation. Here, we investigate what role a language-independent phonological universal, the sonority sequencing principle (SSP), may also play. Participants were presented with an unsegmented speech stream with non-English word onsets that juxtaposed adherence to the SSP with transitional probabilities. Participants favored using the SSP in assessing word-hood, suggesting that the SSP represents a potentially powerful cue for word segmentation. To ensure the SSP influenced the segmentation process (i.e., during learning), we presented two additional groups of participants with either (a) no exposure to the stimuli prior to testing or (b) the same stimuli with pauses marking word breaks. The SSP did not influence test performance in either case, suggesting that the SSP is important for word segmentation during the learning process itself. Moreover, the fact that SSP-independent segmentation of the stimulus occurred (in the latter control condition) suggests that universals are best understood as biases rather than immutable constraints on learning.     

Speech segmentation is facilitated by visual cues

Evidence from infant studies indicates that language learning can be facilitated by multimodal cues. We extended this observation to adult language learning by studying the effects of simultaneous visual cues (nonassociated object images) on speech segmentation performance. Our results indicate that segmentation of new words from a continuous speech stream is facilitated by simultaneous visual input that it is presented at or near syllables that exhibit the low transitional probability indicative of word boundaries. This indicates that temporal audio-visual contiguity helps in directing attention to word boundaries at the earliest stages of language learning. Off-boundary or arrhythmic picture sequences did not affect segmentation performance, suggesting that the language learning system can effectively disregard noninformative visual information. Detection of temporal contiguity between multimodal stimuli may be useful in both infants and second-language learners not only for facilitating speech segmentation, but also for detecting word–object relationships in natural environments.     

Speech segmentation by statistical learning is supported by domain-general processes within working memory

The purpose of this study was to examine the extent to which working memory resources are recruited during statistical learning (SL). Participants were asked to identify novel words in an artificial speech stream where the transitional probabilities between syllables provided the only segmentation cue. Experiments 1 and 2 demonstrated that segmentation performance improved when the speech rate was slowed down, suggesting that SL is supported by some form of active processing or maintenance mechanism that operates more effectively under slower presentation rates. In Experiment 3 we investigated the nature of this mechanism by asking participants to perform a two-back task while listening to the speech stream. Half of the participants performed a two-back rhyme task designed to engage phonological processing, whereas the other half performed a comparable two-back task on un-nameable visual shapes. It was hypothesized that if SL is dependent only upon domain-specific processes (i.e., phonological rehearsal), the rhyme task should impair speech segmentation performance more than the shape task. However, the two loads were equally disruptive to learning, as they both eradicated the benefit provided by the slow rate. These results suggest that SL is supported by working-memory processes that rely on domain-general resources.     

Brain tumor detection using fusion of hand crafted and deep learning features

The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.     

Using Predictability for Lexical Segmentation

This study investigates a strategy based on predictability of consecutive sub‐lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub‐lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state‐of‐the‐art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in‐depth analysis of the model, investigating the conditions affecting the usefulness of the strategy.     

高校辅导员的边界分割偏好与工作-非工作冲突:边界分割管理策略的调节作用

采用问卷调查法,对312名高校辅导员进行调查,探讨边界分割偏好、组织分割供给和个人边界分割策略与工作-非工作冲突的关系。结果表明:(1)辅导员的边界分割偏好与工作-非工作冲突正相关;(2)组织分割供给和个人边界分割策略均能减弱边界分割偏好与工作-非工作冲突之间的正相关。本研究提示高校应在不影响工作的前提下尽量向辅导员提供更多的组织分割供给,同时辅导员也可以通过灵活运用边界分割策略,进行自主的工作-非工作边界管理。     

The link between statistical segmentation and word learning in adults

Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this possible link in adults by following a statistical segmentation exposure phase with an artificial lexicon learning phase. Participants were able to learn all novel object-label pairings, but pairings were learned faster when labels contained high probability (word-like) or non-occurring syllable transitions from the statistical segmentation phase than when they contained low probability (boundary-straddling) syllable transitions. This suggests that, for adults, labels inconsistent with expectations based on statistical learning are harder to learn than consistent or neutral labels. In contrast, a previous study found that infants learn consistent labels, but not inconsistent or neutral labels.     

测验与反馈在分段视频学习中的作用

分段原则是多媒体和视频学习中通过分段促进学习的重要原则。本研究探讨了测验和反馈对分段的影响。实验1对比了观看分段视频和在分段中进行测验两种情境,结果表明分段中测验会提高学习表现。实验2对比了分段视频、分段中测验和分段中测验并提供反馈三种情境。结果发现分段组与分段测验组之间没有差异,接受反馈的学习者学习表现最好。结果表明在分段视频中测验和反馈具有积极效应,对多媒体和线上学习具有潜在的意义。     

The role of perceptual salience during the segmentation of connected speech

Statistical learning allows listeners to track transitional probabilities among syllable sequences and use these probabilities for subsequent speech segmentation. Recent studies have shown that other sources of information, such as rhythmic cues, can modulate the dependencies extracted via statistical computation. In this study, we explored how syllables made salient by a pitch rise affect the segmentation of trisyllabic words from an artificial speech stream by native speakers of three different languages (Spanish, English, and French). Results showed that, whereas performance of French participants did not significantly vary across stress positions (likely due to language-specific rhythmic characteristics), the segmentation performance of Spanish and English listeners was unaltered when syllables in word-initial and word-final positions were salient, but it dropped to chance level when salience was on the medial syllable. We argue that pitch rise in word-medial syllables draws attentional resources away from word boundaries, thus decreasing segmentation effectiveness.     

An efficient cervical disease diagnosis approach using segmented images and cytology reporting

Cervical cancer is the second most common cancer in women globally. A computer aided cervical disease diagnosis system that can relieve pressure on medical experts and save the cost is proposed. To implement our approach in the reality of cervical diseases diagnosis, a multi-modal framework is designed for three kinds of cervical diseases diagnosis that integrates uterine cervix images, Thinprep Cytology Test, human papillomavirus test, and patients’ age. However, too many features increase memory storage costs and computational costs, and it affects the spread of this system in poor areas. Feature selection not only eliminates redundant or irrelevant features but also finds the factors that influence the disease most first is performed in multi-modal frameworks for cervical diseases diagnosis. The detailed process of the method is as follows: first, according the representative color, an efficient image segmentation algorithm is developed; then from three different types of segmented images, we extract color features and texture features for interpreting uterine cervix images; next, Boruta algorithm is applied to feature selection; finally, the performance of Random Forests that utilizes selected features for cervical disease diagnosis is investigated. In the experiment, the proposed multi-modal diagnostic approach gives the final diagnosis for three different kinds of cervical diseases with 83.1% accuracy, which significantly outperforms methods using any single source of information alone. The validation cohort is applied to validate the efficiency of our method, and the performance of random forest obtained by using only 1.2% of features is like or even better than using 100% of features.