Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods

The roles of primary-level health workers in delivering mental healthcare in India

This research explored the history, effectiveness and feasibility of primary-level health workers (PHWs) in delivering care for mental, neurological and substance use (MNS) disorders in India, to better inform the organisation and delivery of mental health services at primary care and community levels. This thesis examined evidence for the effectiveness of PHWs in mental healthcare in low- and middle-income countries (LMICs) (Cochrane review – 38 included studies), and then focused on India. Seventeen oral history interviews described the experiences of integrating mental healthcare into primary care and 72 case-studies explored government and non-governmental models of PHW-delivered mental healthcare initiatives and their human resources. PHWs can be effective in delivering care for MNS disorders in LMICs. The case studies identified heterogeneous collaborative care models in India, most of which were delivered through community- rather than government- primary care. Other models (training and referral) which have less evidence for effectiveness were more widespread, and included the government model which was perceived as having ‘failed’. A new model was identified: community outreach services which were specialist-led but PHW-delivered. LHWs and care managers seemed more feasible and appropriate care managers than PHC doctors across models and provided more holistic psychosocial support. Specialists were valuable for PHWs’ and care managers’ training and ongoing support. Barriers to mental health care integration are discussed. Future research priorities are to assess whether variations of collaborative models are similarly effective to those described in HICs and whether these are feasible and effective if implemented at scale. Priorities for improving the DMHP would be to consider deploying care managers and LHWs and reorient as well as incentivise specialists to support them. Better inter-sectoral collaborations, health system strengthening and technical support at central- and state-government levels may improve leadership, implementation and evaluation of mental healthcare integration into primary care across India

Non-specialist health worker interventions for the care of mental, neurological and substance-abuse disorders in low- and middle-income countries.

BACKGROUND: Many people with mental, neurological and substance-use disorders (MNS) do not receive health care. Non-specialist health workers (NSHWs) and other professionals with health roles (OPHRs) are a key strategy for closing the treatment gap. OBJECTIVES: To assess the effect of NSHWs and OPHRs delivering MNS interventions in primary and community health care in low- and middle-income countries. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (including the Cochrane Effective Practice and Organisation of Care (EPOC) Group Specialised Register) (searched 21 June 2012); MEDLINE, OvidSP; MEDLINE In Process & Other Non-Indexed Citations, OvidSP; EMBASE, OvidSP (searched 15 June 2012); CINAHL, EBSCOhost; PsycINFO, OvidSP (searched 18 and 19 June 2012); World Health Organization (WHO) Global Health Library (searched 29 June 2012); LILACS; the International Clinical Trials Registry Platform (WHO); OpenGrey; the metaRegister of Controlled Trials (searched 8 and 9 August 2012); Science Citation Index and Social Sciences Citation Index (ISI Web of Knowledge) (searched 2 October 2012) and reference lists, without language or date restrictions. We contacted authors for additional studies. SELECTION CRITERIA: Randomised and non-randomised controlled trials, controlled before-and-after studies and interrupted-time-series studies of NSHWs/OPHR-delivered interventions in primary/community health care in low- and middle-income countries, and intended to improve outcomes in people with MNS disorders and in their carers. We defined an NSHW as any professional health worker (e.g. doctors, nurses and social workers) or lay health worker without specialised training in MNS disorders. OPHRs included people outside the health sector (only teachers in this review). DATA COLLECTION AND ANALYSIS: Review authors double screened, double data-extracted and assessed risk of bias using standard formats. We grouped studies with similar interventions together. Where feasible, we combined data to obtain an overall estimate of effect. MAIN RESULTS: The 38 included studies were from seven low- and 15 middle-income countries. Twenty-two studies used lay health workers, and most addressed depression or post-traumatic stress disorder (PTSD). The review shows that the use of NSHWs, compared with usual healthcare services: 1. may increase the number of adults who recover from depression or anxiety, or both, two to six months after treatment (prevalence of depression: risk ratio (RR) 0.30, 95% confidence interval (CI) 0.14 to 0.64; low-quality evidence); 2. may slightly reduce symptoms for mothers with perinatal depression (severity of depressive symptoms: standardised mean difference (SMD) -0.42, 95% CI -0.58 to -0.26; low-quality evidence); 3. may slightly reduce the symptoms of adults with PTSD (severity of PTSD symptoms: SMD -0.36, 95% CI -0.67 to -0.05; low-quality evidence); 4. probably slightly improves the symptoms of people with dementia (severity of behavioural symptoms: SMD -0.26, 95% CI -0.60 to 0.08; moderate-quality evidence); 5. probably improves/slightly improves the mental well-being, burden and distress of carers of people with dementia (carer burden: SMD -0.50, 95% CI -0.84 to -0.15; moderate-quality evidence); 6. may decrease the amount of alcohol consumed by people with alcohol-use disorders (drinks/drinking day in last 7 to 30 days: mean difference -1.68, 95% CI -2.79 to -0.57); low-quality evidence).It is uncertain whether lay health workers or teachers reduce PTSD symptoms among children. There were insufficient data to draw conclusions about the cost-effectiveness of using NSHWs or teachers, or about their impact on people with other MNS conditions. In addition, very few studies measured adverse effects of NSHW-led care - such effects could impact on the appropriateness and quality of care. AUTHORS' CONCLUSIONS: Overall, NSHWs and teachers have some promising benefits in improving people's outcomes for general and perinatal depression, PTSD and alcohol-use disorders, and patient- and carer-outcomes for dementia. However, this evidence is mostly low or very low quality, and for some issues no evidence is available. Therefore, we cannot make conclusions about which specific NSHW-led interventions are more effective

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure

Transient thermal effects in solid noble gases as materials for the detection of Dark Matter

The transient phenomena produced in solid noble gases by the stopping of the recoils resulting from the elastic scattering processes of WIMPs from the galactic halo were modelled, as dependencies of the temperatures of lattice and electronic subsystems on the distance to the recoil's trajectory, and time from its passage. The peculiarities of these thermal transients produced in Ar, Kr and Xe were analysed for different initial temperatures and WIMP energies, and were correlated with the characteristics of the targets and with the energy loss of the recoils. The results were compared with the thermal spikes produced by the same WIMPs in Si and Ge. In the range of the energy of interest, up to tens of keV for the self-recoil, local phase transitions solid - liquid and even liquid - gas were found possible, and the threshold parameters were established.Comment: Minor corrections and updated references; accepted to JCA

Толерантный подход к теме греко-турецких взаимоотношений в новейшей греческой прозе

Целью данной статьи стало рассмотрение нового видения дихотомии «грек-турок» в романах греческих писателей второй половины ХХ века

A large meteoritic event over Antarctica ca. 430 ka ago inferred from chondritic spherules from the Sør Rondane Mountains

Large airbursts, the most frequent hazardous impact events, are estimated to occur orders of magnitude more frequently than crater-forming impacts. However, finding traces of these events is impeded by the difficulty of identifying them in the recent geological record. Here, we describe condensation spherules found on top of Walnumfjellet in the Sør Rondane Mountains, Antarctica. Affinities with similar spherules found in EPICA Dome C and Dome Fuji ice cores suggest that these particles were produced during a single-asteroid impact ca. 430 thousand years (ka) ago. The lack of a confirmed crater on the Antarctic ice sheet and geochemical and 18O-poor oxygen isotope signatures allow us to hypothesize that the impact particles result from a touchdown event, in which a projectile vapor jet interacts with the Antarctic ice sheet. Numerical models support a touchdown scenario. This study has implications for the identification and inventory of large cosmic events on Earth

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods