Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments

STUDY OF IN VITRO ANTI-OXIDANT AND ANTI-DIABETIC ACTIVITY BY MUSSAENDA MACROPHYLLA ROOT EXTRACTS

Objective: The systematic study of effective alternative anti-diabetic drugs has great importance to manage diabetes as well as other oxidative stress-related diseases. According to previous research, root and bark of Mussaenda macrophylla plant has anti-microbial, anti-coagulant, anti-inflammatory, and hepatoprotective activity. Ethnomedicinal data shows that Mussaenda macrophylla is used to treat diabetes as well as oxidative stress. The objective of this research is to investigate in vitro anti-diabetic and anti-oxidant activity of root extract of Mussaenda macrophylla. Methods: DPPH free radical scavenging assay was used to detect anti-oxidant potency of ethanol and methanol root extract of the plant and expressed as % of radicle inhibition. Anti-diabetic activity was determined by the glucose diffusion method using a glucose oxidase kit and results were expressed as mean±SD. Results: The ethanol root extract at the concentration of 50 mg/ml and 100 mg/ml showed better glucose diffusion inhibition than that of methanol extract at the same concentration on increasing time interval. Ethanol extract at the concentration 100 µg/ml displayed better DPPH scavenging activity (89.83±0.19 %) than that of methanol extract (86.61±0.75%). Conclusion: This study concluded that ethanol and methanol root extract of Mussenda macrophylla have potent anti-diabetic as well as anti-oxidant activity but further advance research is necessary in the animal model

Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named “Hemo-Net” has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications

ANALGESIC ACTIVITY OF BARK AND LEAVES OF FICUS RELIGIOSA L. FROM NEPAL

Objective: Because of adverse side effects, caused by NSAIDs, tolerance, and dependence induced by opiates, the use of these analgesic agents has not been successful in all cases. Therefore, alternative analgesic drugs from plant sources are the new target now days. The objective of this study was to evaluate the analgesic activity of ethanolic extracts of stem barks and leaves of Ficus religiosa. Methods: The analgesic activity of ethanolic extract of stem barks and leaves was evaluated in the Swiss albino mice model using acetic acid-induced writing response and Eddy’s hot plate method. Analgesic activity was demonstrated with the percentage inhibition of acetic acid induced writings and the percentage increased in latency time of paw licking. The potency of test extracts was compared with standard drug, Diclofenac. Results: Ethanolic extract of leaves and bark of F. religiosa showed potential analgesic activity from both methods. From Eddy’s hot plate model, it was observed that the percentage of increased latency time at 90 min by ethanolic extract of leaves and stem bark was found to be 70.81 % (8.54 min) and 70.78 % (8.53 min) respectively at a dose of 400 mg/kg. Both of these results are statistically significant (p<0.05) as compared to the test group. Furthermore, both of these extracts showed the dose-dependent and time-dependent increased in latency time and these results are compared to that of standard drug Diclofenac. Similarly, ethanolic extract of leaves and stem at 400 mg/kg significantly inhibited the number of writhings induced by acetic acid. The percentage inhibition of writhings by ethanolic extract of leaves at a dose of 400 mg/kg was 68.47 % which was similar to that of standard drug Diclofenac (68.47 %). However, ethanolic extract of bark showed relatively lower percentage inhibition (60.79 %) as compared to leaf extract and standard, but the result was significant as compared to that of the test group (p<0.05). Conclusion: Ethanolic extracts of F. religiosa stem bark and leaf possess both central and peripheral analgesic properties and these effects may be beneficial for the management of pain

Surface Plasmon Polariton: Dispersion and Excitation

Surface Plasmon Polaritons (SPPs) are the excitation of the electromagnetic field at the interface between metal and dielectric due to coupling of surface plasma with the photon. The dispersion relation of SPPs wave vector with the photon wave vector in free space has been established and found that SPPs momentum is greater than that of photon for given frequency of light shining on the interface. The properties of SPPs like as propagation length along the metal-dielectric interface, penetration depth of the SPPs field intensity into both medium and the complex dielectric function of the metal which is a function of frequency of photon hitting on it have been studied. Since due to mismatch momentum between photon and SPPs, different techniques using prism and grating on the metal surface to erase the momentum gap between the SPPs and photon have been described briefly, So the excitation of the SPPs can be done for various range of incoming photon energy

Mitigating the current energy crisis in Nepal with renewable energy sources

Nepal has been suffering from a serious energy crisis for decades. It has severely affected its economic, social and political developments. Owing to the continuously evolving energy situation in Nepal, and the recent progress in renewable energy technologies, this study aims to provide an up to date perspective on the current energy crisis in Nepal. In particular, the current energy production and consumption profiles are reviewed, and the main factors contributing to a widening gap between the energy supply and demand are identified. These factors concern delayed and overpriced hydropower projects, outdated and insufficient energy infrastructure, transmission and distribution losses, energy theft, deficient energy management, lack of energy conservation, low efficiency of equipment, unsustainable energy pricing strategies and unsatisfying energy market regulations. Other essential factors worsening the energy crisis can be attributed to specific geographical and geopolitical problems, the strong dependence on energy imports, and inadequate exploitation of the vast amounts of renewable energy resources. The status of existing and planned large hydropower projects is summarized. The recent policies and investment initiatives of the Nepalese government to support green and sustainable energy are discussed. Furthermore, a long-term outlook on the energy situation in Nepal is outlined using the energy modeling software LEAP in order to show how to exploit the tremendous renewable energy resources in Nepal. Our findings suggest that renewable resources are crucial not only for mitigating the present energy crisis, but also to ultimately provide energy independence for Nepal by establishing reliable and secure sources of energy

Predicting missing pairwise preferences from similarity features in group decision making

In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost importance, since many experts provide incomplete preferences. In this paper, we propose a new method called the entropy-based method for estimating the missing values in the FPR. We compared the accuracy of our algorithm for predicting the missing values with the best candidate algorithm from state of the art achievements. In the proposed entropy-based method, we took advantage of pairwise preferences to achieve good results by storing extra information compared to single rating scores, for example, a pairwise comparison of alternatives vs. the alternative’s score from one to five stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and thus the solution for the matrix factorization can be expressed in the form of latent expert features and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in the same latent feature space. By virtue of this embedding, another novelty of our approach is to use the similarity of experts, as well as the similarity between alternatives, to infer the missing values even when only minimal data are available for some alternatives from some experts. Note that current approaches may fail to provide any output in such cases. Apart from estimating missing values, another salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives. It is worth mentioning that ranking alternatives have many possible applications in GDM, especially in group recommendation systems (GRS)

Predicting missing pairwise preferences from similarity features in group decision making

In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost importance, since many experts provide incomplete preferences. In this paper, we propose a new method called the entropy-based method for estimating the missing values in the FPR. We compared the accuracy of our algorithm for predicting the missing values with the best candidate algorithm from state of the art achievements. In the proposed entropy-based method, we took advantage of pairwise preferences to achieve good results by storing extra information compared to single rating scores, for example, a pairwise comparison of alternatives vs. the alternative’s score from one to five stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and thus the solution for the matrix factorization can be expressed in the form of latent expert features and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in the same latent feature space. By virtue of this embedding, another novelty of our approach is to use the similarity of experts, as well as the similarity between alternatives, to infer the missing values even when only minimal data are available for some alternatives from some experts. Note that current approaches may fail to provide any output in such cases. Apart from estimating missing values, another salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives. It is worth mentioning that ranking alternatives have many possible applications in GDM, especially in group recommendation systems (GRS)

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments