Learning Opposites Using Neural Networks

Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two types of the "opposites" have been defined in the literature, namely \textit{type-I} and \textit{type-II}. The former are linear in nature and applicable to the variable space, hence easy to calculate. On the other hand, type-II opposites capture the "oppositeness" in the output space. In fact, type-I opposites are considered a special case of type-II opposites where inputs and outputs have a linear relationship. However, in many real-world problems, inputs and outputs do in fact exhibit a nonlinear relationship. Therefore, type-II opposites are expected to be better in capturing the sense of "opposition" in terms of the input-output relation. In the absence of any knowledge about the problem at hand, there seems to be no intuitive way to calculate the type-II opposites. In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs). We first perform \emph{opposition mining} on the sample data, and then use the mined data to learn the relationship between input $x$ and its opposite $\breve{x}$. We have validated our algorithm using various benchmark functions to compare it against an evolving fuzzy inference approach that has been recently introduced. The results show the better performance of a neural approach to learn the opposites. This will create new possibilities for integrating oppositional schemes within existing algorithms promising a potential increase in convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201

Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

Prevalence of insulin induced lipodystrophy in patients with diabetes mellitus in a tertiary care centre: a cross sectional study

Background: Diabetes Mellitus is a spectrum of common metabolic disorders whose management mainly lies in treating the patients with oral hypoglycaemic drugs and insulin along with the dietary and lifestyle modifications. Lipodystrophy is the most neglected adverse drug effect caused by injecting insulin. The main objective of this study was to assess the prevalence of lipodystrophy at the insulin injection sites in patients suffering from diabetes mellitus (Type 1 and Type 2).Methods: A cross-sectional study was conducted in the Department of Endocrinology on 250 diabetic patients taking insulin injections based on inclusion and exclusion criteria. The demographic features and anthropometric measurements were noted. Insulin injection sites were examined clinically by inspection and palpation for presence of swelling like lipodystrophy, injection marks and signs of allergy like erythema etc. Lipodystrophy was graded from 0-3 and denoted as lipohypertrophy or lipoatrophy. The results were tabulated and presented accordingly.Results: In this study, out of 250 patients 17 (6.8%) patients presented with insulin induced lipodystrophy. Lipohypertrophy was the most common presentation and only one case presented with lipoatrophy.Conclusions: It can be concluded from the present study that lipodystrophy which is an important adverse effect due to insulin injection needs to be monitored regularly in every patient taking insulin for better control of glucose levels

Assessment of knowledge, attitude and practice of insulin injection among subjects with diabetes mellitus

Background: Diabetes mellitus (DM) is a spectrum of common metabolic disorders whose management mainly lies in treating the patients with oral hypoglycemic drugs and insulin along with the dietary and lifestyle modifications. Insulin is administered most subcutaneously. As the insulin injection sites are relatively painless, patients tend to inject in the same area repeatedly rather than moving to a newer site and increase risk for development of injection site reactions like lipodystrophy and impairment of glycemic control. Hence, it is utmost important for every diabetic patient and their relatives who would inject the insulin injection to be aware of appropriate manner of insulin injection. This helps in maintaining adequate glycemic control in diabetic patients. The main objective of this study was to assess the knowledge, attitude and practice of insulin injection technique among the diabetic patients.Methods: A cross-sectional study was conducted in the department of Endocrinology on 250 diabetic patients taking insulin injections based on inclusion and exclusion criteria. A validated questionnaire was administered to patients to answer in order to assess their knowledge, attitude and practice about technique of insulin injection.Results: In this study, it was found that 90% of the patients were aware of rotating the injection site, whereas only 40% of the patients were aware of appropriate time duration (5-10 sec for syringes/counting 1-15 for releasing the pen) required for resting the syringe/pen needle inside the skin. Only 48% of the patients used to remove the air bubble prior to injection, 57% pinched the injection site before injecting, 20% rubbed the injection site after injection and 30% used to wash hands prior to injection. Hypoglycemia was the most common adverse effect noted in 54% of patients.Conclusions: It can be concluded from the present study that every patient and his/her attendant needs to be educated and trained appropriately regarding technique of injecting insulin injection for betterment of their health. 

Methimazole induced lichenoid eruptions: an unusual case

This is a case report of a 31-year-old male presented to the Endocrinology outpatient department of our hospital with hyperthyroidism and was prescribed tablet methimazole 30mg once daily and tablet propranolol 40mg once daily. After 3 months, the patient complained of violaceous papular lesions on both the extensor aspect of the arms and legs. Physical examination was remarkable for acute onset, raised, itchy, violaceous papular lesions over the defined areas. The drug methimazole was suspected to cause lichenoid drug eruptions and was withdrawn. This case illustrates methimazole otherwise an efficacious and widely used anti thyroid drug is an agent capable of inducing lichenoid eruptions. However in future the monitoring of methimazole is essential for such adverse reaction