Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.

/pdf/a-survey-on-image-data-augmentation-for-deep-learning-4ru1bj2hbb.pdf

A survey on Image Data Augmentation for Deep Learning

CA : A Cancer Journal for Clinicians

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Pedagosko i psiholosko testiranje i procjenjivanje spadaju među najvažnije doprinose znanosti o ponasanju nasem drustvu i pružaju temeljna i znacajna poboljsanja u odnosu na ranije postupke. Iako se ne može ustvrditi da su svi testovi dovoljno usavrseni niti da su sva testiranja razborita i korisna, postoji velika kolicina informacija koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima. Pravilna upotreba testova može dovesti do boljih odluka o pojedincima i programima nego sto bi to bio slucaj bez njihovog koristenja, a također i ukazati na put za siri i pravedniji pristup obrazovanju i zaposljavanju. Međutim, losa upotreba testova može dovesti do zamjetne stete nanesene ispitanicima i drugim sudionicima u procesu donosenja odluka na temelju testovnih podataka. Cilj Standarda je promoviranje kvalitetne i eticne upotrebe testova te uspostavljanje osnovice za ocjenu kvalitete postupaka testiranja. Svrha objavljivanja Standarda je uspostavljanje kriterija za evaluaciju testova, provedbe testiranja i posljedica upotrebe testova. Iako bi evaluacija prikladnosti testa ili njegove primjene trebala ovisiti prvenstveno o strucnim misljenjima, Standardi pružaju okvir koji osigurava obuhvacanje svih relevantnih pitanja. Bilo bi poželjno da svi autori, sponzori, nakladnici i korisnici profesionalnih testova usvoje Standarde te da poticu druge da ih također prihvate.

Standards for educational and psychological testing

https://www.commed.vcu.edu/Chronic_Disease/2012/cochranrvwrschbias.pdf

The Cochrane Collaboration's tool for assessing risk of bias in randomised trials

Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81\(^{\circ }\) and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment.

/pdf/automatic-c-plane-detection-in-pelvic-floor-transperineal-28rrxg8yd8.pdf

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

Inflammatory Response after Fascial Reconstruction of Abdominal Wall Defects with Porcine Dermal Collagen and Polypropylen in Rats

Objective: Glucagon-like peptide-1 (GLP-1) increases surfactant protein expression in type 2 pneumocytes Herein, we determine if transplacental GLP-1 treatment accelerates lung growth in the fetal rabbit model of congenital diaphragmatic hernia (DH) Methods: Time-mated does had an induction of DH on day 23 followed by daily GLP-1 or placebo injection until term At that time, the does were weighed, fetal blood was obtained for GLP-1 assay, and the lungs were dissected Fetal outcome measures were lung-to-body-weight ratio (LBWR), morphometry, and Ki67 and surfactant protein B (SPB) expression Results: Maternal weight loss in the GLP-1 group was 71% Fetal survival was lower in GLP-1 fetuses compared to placebo controls (27/85, 32% vs 35/57, 61%; p Conclusion: GLP-1 at this dosage improves peripheric pulmonary vessel morphology in intra-acinar vessels with no effect on airway morphometry but with significant maternal and fetal side effects Thus, it is an unlikely medical strategy

The Effect of Transplacental Administration of Glucagon-Like Peptide-1 on Fetal Lung Development in the Rabbit Model of Congenital Diaphragmatic Hernia.

This report describes a case of a uterine cystic myometrial lesion in a 16-year-old adolescent girl presenting with pelvic pain and severe progressively worsening dysmenorrhoea. Patient's symptoms, ultrasound and MRI were suggestive of juvenile cystic adenomyosis (JCA). Medical treatment and alcohol sclerotherapy had a moderate and transient effect. The symptoms rapidly recurred, and the lesion was successfully excised via laparoscopic surgery. The treatment of JCA depends on patient's age, the symptoms' severity and the cyst location. Although rare, juvenile cystic adenomyosis should be considered in young women with severe dysmenorrhoea.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajum.12171

Jan Deprest

Papers

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

Inflammatory Response after Fascial Reconstruction of Abdominal Wall Defects with Porcine Dermal Collagen and Polypropylen in Rats

The Effect of Transplacental Administration of Glucagon-Like Peptide-1 on Fetal Lung Development in the Rabbit Model of Congenital Diaphragmatic Hernia.

Juvenile cystic adenomyosis: A case report and review of the literature.

56: Neonatal morbidity in fetuses with severe isolated congenital diaphragmatic hernia (CDH) in the FETO era