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Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle diseases because many diseases contain different perimysium inflammation. However, it remains as a challenging task due to the complex appearance of the perymisum morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-based methods to capture this long-range context information. In this paper, we propose a novel spatial clockwork recurrent neural network (spatial CW-RNN) to address those issues. Specifically, we split the entire image into a set of non-overlapping image patches, and the semantic dependencies among them are modeled by the proposed spatial CW-RNN. Our method directly takes the 2D structure of the image into consideration and is capable of encoding the context information of the entire image into the local representation of each patch. Meanwhile, we leverage on the structured regression to assign one prediction mask rather than a single class label to each local patch, which enables both efficient training and testing. We extensively test our method for perimysium segmentation using digitized muscle microscopy images. Experimental results demonstrate the superiority of the novel spatial CW-RNN over other existing state of the arts.

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.

Compact binary representations of histopathology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality of the query image based on the retrieved similar cases, thereby providing support for medical diagnosis. They also allow for efficient managing of large-scale image databases because of a low storage requirement. However, the effectiveness of binary representations heavily relies on the visual descriptors that represent the semantic information in the histopathological images. Traditional approaches with hand-crafted visual descriptors might fail due to significant variations in image appearance. Recently, deep learning architectures provide promising solutions to address this problem using effective semantic representations. In this paper, we propose a Deep Convolutional Hashing (DCH) method that can be trained "point-wise" to simultaneously learn both semantic and binary representations of histopathological images. Specifically, we propose a convolutional neural network (CNN) that introduces a latent binary encoding (LBE) layer for low dimensional feature embedding to learn binary codes. We design a joint optimization objective function that encourages the network to learn discriminative representations from the label information, and reduce the gap between the real-valued low dimensional embedded features and desired binary values. The binary encoding for new images can be obtained by forward propagating through the network and quantizing the output of the LBE layer. Experimental results on a large-scale histopathological image dataset demonstrate the effectiveness of the proposed method.

Background: Lack of knowledge and awareness about oral cancer, its risk factors and negligence of the early warning signs play crucial role in raising the incidence of the disease. The present study was carried out to evaluate the awareness of oral cancer among patients visiting Kantipur Dental College, Kathmandu, Nepal. Methods:The cross-sectional study was done in 471 patients from 15-85 years. Self administered questionnaire was prepared which comprised of knowledge of oral cancer, source of information, its early signs and symptoms along with the awareness of its risk factors.Results: Most of the participants (41.80%) had not heard of oral cancer. 31.60% recognized tobacco smoking and tobacco chewing as the chief risk factor with 15.50% and 10.80% of participants who identified white patch and red patch as early sign of oral cancer respectively. Pearson's chi square test was used which showed statistically significant association of total mean knowledge score and awareness score with age, education level and occupation (p<0.05).Conclusions: This study done in dental patients showed lack of knowledge and awareness in general public about oral cancer. There seem to be a need for more planned awareness programs through newspapers, radio, television and health campaigns regarding the association of habits in the development of oral cancer and benefits of detecting oral cancer at early stage for better prognosis.

Diseased skeletal muscle expresses mononuclear cell infiltration in the regions of perimysium. Accurate annotation or segmentation of perimysium can help biologists and clinicians to determine individualized patient treatment and allow for reasonable prognostication. However, manual perimysium annotation is time consuming and prone to inter-observer variations. Meanwhile, the presence of ambiguous patterns in muscle images significantly challenge many traditional automatic annotation algorithms. In this paper, we propose an automatic perimysium annotation algorithm based on deep convolutional neural network (CNN). We formulate the automatic annotation of perimysium in muscle images as a pixel-wise classification problem, and the CNN is trained to label each image pixel with raw RGB values of the patch centered at the pixel. The algorithm is applied to 82 diseased skeletal muscle images. We have achieved an average precision of 94% on the test dataset.

Introduction: Substance abuse has become a burning issue among the medical and dental students. Dental students, who later transform into dentists, have a significant role in substance abuse cessation. Thus the study was undertaken to quantify substance abuse among dental students of Kantipur Dental College. Methods: A descriptive cross-sectional study was conducted using pretested self-administered questionnaire among undergraduate and post graduate students of Kantipur Dental College. Convenience sampling was done and sample size was calculated.Results: Study revealed 166 (74.10%) as never smokers, 3 (1.30%) as former smokers and 55 (24.60%) as current smokers. Similarly 97 (43.3%) students never used alcoholic drink, 95 (42.41%) consumed alcohol monthly, 29 (12.95%) consumed alcohol 2-4 times a month and 3 (1.34%) consumed alcohol 2-3 times a week. A total of 78 (35%) students used cannabis.Conclusions: Substantial numbers of students were indulged in deleterious habits of smoking, tobacco and cannabis intake. Students need to be properly counseled to discourage substance abuse and create a healthy society.