MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on prototypes (\eg mean of $K-$shot) for prediction, leading to performance instability. To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information. Inspired by augmentation approaches that perturb data with Gaussian noise for populating low data density regions, we model the mask distribution with a diffusion probabilistic model. We also propose to utilize classifier-free guided mask sampling to integrate category information into the binary mask generation process. Without bells and whistles, our proposed method consistently outperforms state-of-the-art methods on both base and novel classes of the COCO dataset while simultaneously being more stable than existing methods. The source code is available at: https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation

Combination Antifungal Therapy for Cryptococcal Meningitis

Background Combination antifungal therapy (amphotericin B deoxycholate and flucytosine) is the recommended treatment for cryptococcal meningitis but has not been shown to reduce mortality, as compared with amphotericin B alone. We performed a randomized, controlled trial to determine whether combining flucytosine or high-dose fluconazole with high-dose amphotericin B improved survival at 14 and 70 days. Methods We conducted a randomized, three-group, open-label trial of induction therapy for cryptococcal meningitis in patients with human immunodeficiency virus infection. All patients received amphotericin B at a dose of 1 mg per kilogram of body weight per day; patients in group 1 were treated for 4 weeks, and those in groups 2 and 3 for 2 weeks. Patients in group 2 concurrently received flucytosine at a dose of 100 mg per kilogram per day for 2 weeks, and those in group 3 concurrently received fluconazole at a dose of 400 mg twice daily for 2 weeks. Results A total of 299 patients were enrolled. Fewer deaths occurred by days 14 and 70 among patients receiving amphotericin B and flucytosine than among those receiving amphotericin B alone (15 vs. 25 deaths by day 14; hazard ratio, 0.57; 95% confidence interval [CI], 0.30 to 1.08; unadjusted P=0.08; and 30 vs. 44 deaths by day 70; hazard ratio, 0.61; 95% CI, 0.39 to 0.97; unadjusted P=0.04). Combination therapy with fluconazole had no significant effect on survival, as compared with monotherapy (hazard ratio for death by 14 days, 0.78; 95% CI, 0.44 to 1.41; P=0.42; hazard ratio for death by 70 days, 0.71; 95% CI, 0.45 to 1.11; P=0.13). Amphotericin B plus flucytosine was associated with significantly increased rates of yeast clearance from cerebrospinal fluid (−0.42 log10 colony-forming units [CFU] per milliliter per day vs. −0.31 and −0.32 log10 CFU per milliliter per day in groups 1 and 3, respectively; P<0.001 for both comparisons). Rates of adverse events were similar in all groups, although neutropenia was more frequent in patients receiving a combination therapy. Conclusions Amphotericin B plus flucytosine, as compared with amphotericin B alone, is associated with improved survival among patients with cryptococcal meningitis. A survival benefit of amphotericin B plus fluconazole was not found

Establishing and validating noninvasive prenatal testing procedure for fetal aneuploidies in Vietnam

Noninvasive prenatal testing (NIPT) for fetal aneuploidies has been widely adopted in developed countries. Despite the sharp decrease in the cost of massively parallel sequencing, the technical know-how and skilled personnel are still one of the major limiting factors for applying this technology to NIPT in low-income settings. Here, we present the establishment and validation of our NIPT procedure called triSure for detection of fetal aneuploidies.We established the triSure algorithm based on the difference in proportion of fetal and maternal fragments from the target chromosome to all chromosomes. Our algorithm was validated using a published data set and an in-house data set obtained from high-risk pregnant women in Vietnam who have undergone amniotic testing. Several other aneuploidy calling methods were also applied to the same data set to benchmark triSure performance.The triSure algorithm showed similar accuracy to size-based method when comparing them using published data set. Using our in-house data set from 130 consecutive samples, we showed that triSure correctly identified the most samples (overall sensitivity and specificity of 0.983 and 0.986, respectively) compared to other methods tested including count-based, sized-based, RAPIDR and NIPTeR.We have demonstrated that our triSure NIPT procedure can be applied to pregnant women in low-income settings such as Vietnam, providing low-risk screening option to reduce the need for invasive diagnostic tests

Experience in Using Mobile Laboratory for Monitoring and Diagnostics in the Socialist Republic of Vietnam

The aim was to present the experience of using mobile laboratory for monitoring and diagnostics (MLMD) during the epizootiological monitoring of the northern provinces of Vietnam. MLMD was transferred by Federal Service for Surveillance in the Sphere of Consumers Rights Protection and Human Welfare to the Socialist Republic of Vietnam as part of implementation of cooperation programs on combating infectious diseases. The use of MLMD made it possible to obtain new information on the circulation of pathogens of natural-focal infectious diseases on the territory of Vietnam. It also provided the necessary conditions for conducting research using methods of express diagnostics, bacteriological analysis, performing a full cycle of work – from the receipt of samples to the disinfection and destruction of infected material in compliance with the requirements of biological safety in the field. The effectiveness of using mobile laboratories in response to the emergencies of sanitary and epidemiological nature, both to strengthen stationary laboratory bases and to organize diagnostic studies in remote regions, has been shown. The use of MLMD for the diagnosis of COVID‑19 has been an effective component of countering the new coronavirus infection in Vietnam and significantly increased the volume of testing in the country

Identification of diagnostic serum protein profiles of glioblastoma patients

Diagnosis of a glioblastoma (GBM) is triggered by the onset of symptoms and is based on cerebral imaging and histological examination. Serum-based biomarkers may support detection of GBM. Here, we explored serum protein concentrations of GBM patients and used data mining to explore profiles of biomarkers and determine whether these are associated with the clinical status of the patients. Gene and protein expression data for astrocytoma and GBM were used to identify secreted proteins differently expressed in tumors and in normal brain tissues. Tumor expression and serum concentrations of 14 candidate proteins were analyzed for 23 GBM patients and nine healthy subjects. Data-mining methods involving all 14 proteins were used as an initial evaluation step to find clinically informative profiles. Data mining identified a serum protein profile formed by BMP2, HSP70, and CXCL10 that enabled correct assignment to the GBM group with specificity and sensitivity of 89 and 96%, respectively (p < 0.0001, Fischer’s exact test). Survival for more than 15 months after tumor resection was associated with a profile formed by TSP1, HSP70, and IGFBP3, enabling correct assignment in all cases (p < 0.0001, Fischer’s exact test). No correlation was found with tumor size or age of the patient. This study shows that robust serum profiles for GBM may be identified by data mining on the basis of a relatively small study cohort. Profiles of more than one biomarker enable more specific assignment to the GBM and survival group than those based on single proteins, confirming earlier attempts to correlate single markers with cancer. These conceptual findings will be a basis for validation in a larger sample size

A prospective descriptive study of cryptococcal meningitis in HIV uninfected patients in Vietnam - high prevalence of Cryptococcus neoformans var grubii in the absence of underlying disease

<p>Abstract</p> <p>Background</p> <p>Most cases of cryptococcal meningitis occur in patients with HIV infection: the course and outcome of disease in the apparently immunocompetent is much more poorly understood. We describe a cohort of HIV uninfected Vietnamese patients with cryptococcal meningitis in whom underlying disease is uncommon, and relate presenting features of patients and the characteristics of the infecting species to outcome.</p> <p>Methods</p> <p>A prospective descriptive study of HIV negative patients with cryptococcal meningitis based at the Hospital for Tropical Diseases, Ho Chi Minh City. All patients had comprehensive clinical assessment at baseline, were cared for by a dedicated study team, and were followed up for 2 years. Clinical presentation was compared by infecting isolate and outcome.</p> <p>Results</p> <p>57 patients were studied. <it>Cryptococcus neoformans var grubii </it>molecular type VN1 caused 70% of infections; <it>C. gattii </it>accounted for the rest. Most patients did not have underlying disease (81%), and the rate of underlying disease did not differ by infecting species. 11 patients died while in-patients (19.3%). Independent predictors of death were age ≥ 60 years and a history of convulsions (odds ratios and 95% confidence intervals 8.7 (1 - 76), and 16.1 (1.6 - 161) respectively). Residual visual impairment was common, affecting 25 of 46 survivors (54.3%). Infecting species did not influence clinical phenotype or outcome. The minimum inhibitory concentrations of flucytosine and amphotericin B were significantly higher for <it>C. neoformans var grubii </it>compared with <it>C. gattii </it>(p < 0.001 and p = 0.01 respectively).</p> <p>Conclusion</p> <p>In HIV uninfected individuals in Vietnam, cryptococcal meningitis occurs predominantly in people with no clear predisposing factor and is most commonly due to <it>C. neoformans var grubii</it>. The rates of mortality and visual loss are high and independent of infecting species. There are detectable differences in susceptibility to commonly used antifungal drugs between species, but the clinical significance of this is not clear.</p

Evolution by Adapting Surrogates

To deal with complex optimization problems plagued with computationally expensive fitness functions, the use of surrogates to replace the original functions within the evolutionary framework is becoming a common practice. However, the appropriate datacentric approximation methodology to use for the construction of surrogate model would depend largely on the nature of the problem of interest, which varies from fitness landscape and state of the evolutionary search, to the characteristics of search algorithm used. This has given rise to the plethora of surrogate-assisted evolutionary frameworks proposed in the literature with ad hoc approximation/surrogate modeling methodologies considered. Since prior knowledge on the suitability of the data centric approximation methodology to use in surrogate-assisted evolutionary optimization is typically unavailable beforehand, this paper presents a novel evolutionary framework with the evolvability learning of surrogates (EvoLS) operating on multiple diverse approximation methodologies in the search. Further, in contrast to the common use of fitness prediction error as a criterion for the selection of surrogates, the concept of evolvability to indicate the productivity or suitability of an approximation methodology that brings about fitness improvement in the evolutionary search is introduced as the basis for adaptation. The backbone of the proposed EvoLS is a statistical learning scheme to determine the evolvability of each approximation methodology while the search progresses online. For each individual solution, the most productive approximation methodology is inferred, that is, the method with highest evolvability measure. Fitness improving surrogates are subsequently constructed for use within a trust-region enabled local search strategy, leading to the self-configuration of a surrogate-assisted memetic algorithm for solving computationally expensive problems. A numerical study of EvoLS on commonly used benchmark problems and a real-world computationally expensive aerodynamic car rear design problem highlights the efficacy of the proposed EvoLS in attaining reliable, high quality, and efficient performance under a limited computational budget.Published versio