A systematic study of the class imbalance problem in convolutional neural networks

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks and compare frequently used methods to address the issue. Class imbalance refers to significantly different number of examples among classes in a training set. It is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. We define and parameterize two representative types of imbalance, i.e. step and linear. Using three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, we investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental and increases with the extent of imbalance and the scale of a task; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent; (iv) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest; (v) as opposed to some classical machine learning models, oversampling does not necessarily cause overfitting of convolutional neural networks.I den här studien undersöker vi systematiskt effekten av klassobalans på prestandan för klassificering hos konvolutionsnätverk och jämför vanliga metoder för att åtgärda problemet. Klassobalans avser betydlig ojämvikt hos antalet exempel per klass i ett träningsset. Det är ett vanligt problem som har studerats utförligt inom maskininlärning, men tillgången av systematisk forskning inom djupinlärning är starkt begränsad. Vi definerar och parametriserar två representiva typer av obalans, steg och linjär. Med hjälpav tre dataset med ökande komplexitet, MNIST, CTFAR-10 och ImageNet, undersöker vi effekterna av obalans på klassificering och utför en omfattande jämförelse av flera metoder för att åtgärda problemen: översampling, undersampling, tvåfasträning och avgränsning för tidigare klass-sannolikheter. Vår huvudsakliga utvärderingsmetod är arean under mottagarens karaktäristiska kurva (ROC AUC) justerat för multi-klass-syften, eftersom den övergripande noggrannheten är förenad med anmärkningsvärda svårigheter i samband med obalanserade data. Baserat på experimentens resultat drar vi slutsatserna att (i) effekten av klassens obalans påklassificeringprestanda är skadlig och ökar med mängden obalans och omfattningen av uppgiften; (ii) metoden att ta itu med klassobalans som framträdde som dominant i nästan samtliga analyserade scenarier var översampling; (iii) översampling bör tillämpas till den nivå som helt eliminerar obalansen, medan undersampling kan prestera bättre när obalansen bara avlägsnas i en viss utsträckning; (iv) avgränsning bör tillämpas för att kompensera för tidigare sannolikheter när det totala antalet korrekt klassificerade fall är av intresse; (v) i motsats till hos vissa klassiska maskininlärningsmodeller orsakar översampling inte nödvändigtvis överanpassning av konvolutionsnätverk

Metronomic chemotherapy based on Topotecan or Topotecan and cyclophosphamide combination (CyTo) in advanced, pretreated ovarian cancer

Patients with advanced ovarian cancer (OC) have a detrimental prognosis. The options for systemic treatment of advanced OC in later lines of treatment are limited by the availability of active therapies and their applicability to often fragile, exhausted patients with poor performance status. Metronomic chemotherapy (MC) is a concept of a continuous administration of cytotoxic drugs, which is characterized by multidirectional activity (anti-proliferative, anti-angiogenic, and anti-immunosuppressive) and low toxicity. We have performed a retrospective analysis of consecutive, advanced, chemo-refractory OC patients treated with MC based on single-agent topotecan (1 mg p.o. q2d) or on a topotecan (1 mg q2d) and cyclophosphamide (50 mg p.o. qd) combination (CyTo). Metronomic chemotherapy demonstrated promising activity, with 72% and 86% of patients achieving biochemical or objective disease control and 18% and 27% of patients achieving a biochemical or objective response, respectively. The median PFS in the whole population was 3.65 months, but the median PFS in patients with a biochemical response to MC (18.2% of patients) reached 10.7 months. The study also suggested that overweight or obese patients had significantly better outcomes on MC than patients with BMI 2. This article is the first report in the literature on metronomic chemotherapy based on a topotecan + cyclophosphamide combination (CyTo). The CyTo regimen demonstrated safety, clinical activity, and potential broad clinical applicability in advanced OC patients and will be evaluated in a forthcoming clinical trial

Identification of Leaf Rust Resistance Genes in Selected Wheat Cultivars and Development of Multiplex PCR

Ten leading wheat cultivars originating from the Plant Breeding and Acclimatization Institute (IHAR) - National Research Institute (Poland) and the Department of Gene Bank (Czech Republic) were used to establish a field experiment in 2017 and 2018 at the Dłoń Experimental Farm. The analyzed wheat genotypes were characterized by diversified field resistance to leaf rust. Jubilatka, Thatcher and Sparta were the most resistant cultivars in field conditions in both 2017 and 2018. The aim of the work was to identify the Lr11, L13, Lr16 and Lr26 genes encoding resistance to leaf rust using molecular SSR markers (wmc24, wmc261, Xgwm630, Xwmc764 and P6M12) and to develop multiplex PCR conditions to accelerate identification of these genes. Markers of three leaf rust resistance genes have been identified simultaneously in these cultivars. Jubilatka, Thatcher and Sparta cultivars may serve as a good source of the analyzed leaf rust resistance genes. In addition, multiplex PCR conditions have been developed for the simultaneous identification of the Lr11 and Lr16 and Lr11 and Lr26 gene pairs