Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.Comment: Accepted in the Medical Image Analysis journa

Top predators, habitat complexity and the biodiversity of litter-dwelling ants

Trabalho final de mestrado integrado em Medicina (Geriatria), apresentado á Faculdade de Medicina da Universidade de CoimbraA hipertensão arterial é o principal fator de risco modificável para a morbimortalidade por doenças cardiovasculares, a principal causa de morte a nível mundial. Dado o aumento da sua prevalência com a idade, a hipertensão arterial no idoso é uma patologia cada vez mais frequente. A rigidez arterial e a disfunção endotelial são a base fisiopatológica da hipertensão arterial no idoso, não devendo contudo ser descurada a maior incidência de causas secundárias. O tratamento farmacológico da hipertensão arterial no idoso é recomendado tendo em consideração o seu efeito na redução da mortalidade e morbilidade cardiovascular. O valor-alvo de tensão arterial recomendado para estes doentes é 150/90 mmHg, pela ausência de benefícios com um controlo tensional mais restrito. Não há evidência que suporte a utilização preferencial de uma classe ou combinação farmacológica, devendo o grande enfoque terapêutico ser a redução tensional e não os agentes utilizados. Doentes com comorbilidades ou pertencentes a populações especiais podem apresentar indicações farmacológicas específicas e valores-alvo diferentes. As reações adversas à terapêutica são mais frequentes no idoso. Assim, os idosos hipertensos devem manter vigilância para identificação precoce de reações adversas e aumentar a adesão terapêutica.The arterial hypertension is the main modifiable risk factor for morbimortality of cardiovascular diseases, the main cause of death worldwide. Given the increase of its prevalence with age, the arterial hypertension in the elderly is becoming increasingly frequent. The arterial stiffness and endothelial dysfunction are the pathophysiological basis of the arterial hypertension in the elderly, although it cannot be neglect the higher incidence of secondary causes. The pharmacological treatment of arterial hypertension in the elderly is recommended considering its effects on the reduction of cardiovascular mortality and morbidity. The blood pressure target recommended for these patients is 150/90 mmHg, due to the lack of benefits in a stricter blood pressure control. There is no evidence supporting the preferential utilization of a pharmacological class or combination. The major focus should be on blood pressure reduction and not on the agent used. Patients with comorbidities or from special populations may have specific pharmacologial indications and different target values. The therapy’s adverse reactions are more frequent in the elderly. Thus, the hypertensive elderly must maintain vigilance to identify early the adverse reactions and increase the therapeutic adherenc

cudaMap: a GPU accelerated program for gene expression connectivity mapping

BACKGROUND: Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. RESULTS: cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. CONCLUSION: Emerging ‘omics’ technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap

Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies

BACKGROUND: While the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease. RESULTS: We developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations

Hierarchy and the Provision of Order in International Politics

The anarchic international system is actually heavily structured: communities of states join together for common benefit; strong states form hierarchical relationships with weak states to enforce order and achieve preferred outcomes. Breaking from prior research, we conceptualize structures such as community and hierarchy as properties of networks of states’ interactions that can capture unobserved constraints in state behavior, constraints that may reduce conflict. We offer two claims. One, common membership in trade communities pacifies to the extent that breaking trade ties would entail high switching costs: thus, we expect heavy arms trade, more than most types of commercial trade, to reduce intracommunity conflict. Two, this is driven by hierarchical communities in which strong states can use high switching costs as leverage to constrain conflict between weaker states in the community. We find empirical support for these claims using a time-dependent multilayer network model and a new measure of hierarchy based on network centrality

Study protocol: The Improving Care of Acute Lung Injury Patients (ICAP) study

INTRODUCTION: The short-term mortality benefit of lower tidal volume ventilation (LTVV) for patients with acute lung injury/acute respiratory distress syndrome (ALI/ARDS) has been demonstrated in a large, multi-center randomized trial. However, the impact of LTVV and other critical care therapies on the longer-term outcomes of ALI/ARDS survivors remains uncertain. The Improving Care of ALI Patients (ICAP) study is a multi-site, prospective cohort study that aims to evaluate the longer-term outcomes of ALI/ARDS survivors with a particular focus on the effect of LTVV and other critical care therapies. METHODS: Consecutive mechanically ventilated ALI/ARDS patients from 11 intensive care units (ICUs) at four hospitals in the city of Baltimore, MD, USA, will be enrolled in a prospective cohort study. Exposures (patient-based, clinical management, and ICU organizational) will be comprehensively collected both at baseline and throughout patients' ICU stay. Outcomes, including mortality, organ impairment, functional status, and quality of life, will be assessed with the use of standardized surveys and testing at 3, 6, 12, and 24 months after ALI/ARDS diagnosis. A multi-faceted retention strategy will be used to minimize participant loss to follow-up. RESULTS: On the basis of the historical incidence of ALI/ARDS at the study sites, we expect to enroll 520 patients over two years. This projected sample size is more than double that of any published study of long-term outcomes in ALI/ARDS survivors, providing 86% power to detect a relative mortality hazard of 0.70 in patients receiving higher versus lower exposure to LTVV. The projected sample size also provides sufficient power to evaluate the association between a variety of other exposure and outcome variables, including quality of life. CONCLUSION: The ICAP study is a novel, prospective cohort study that will build on previous critical care research to improve our understanding of the longer-term impact of ALI/ARDS, LTVV and other aspects of critical care management. Given the paucity of information about the impact of interventions on long-term outcomes for survivors of critical illness, this study can provide important information to inform clinical practice

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures in breast cancer tissue sections based on convolutional neural networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs). In this paper, we present a method to train and evaluate CNNs to specifically solve these issues in the context of mitosis detection in breast cancer WSIs. First, by combining image analysis of mitotic activity in phosphohistone-H3 (PHH3) restained slides and registration, we built a reference standard for mitosis detection in entire H&E WSIs requiring minimal manual annotation effort. Second, we designed a data augmentation strategy that creates diverse and realistic H&E stain variations by modifying the hematoxylin and eosin color channels directly. Using it during training combined with network ensembling resulted in a stain invariant mitosis detector. Third, we applied knowledge distillation to reduce the computational requirements of the mitosis detection ensemble with a negligible loss of performance. The system was trained in a single-center cohort and evaluated in an independent multicenter cohort from The Cancer Genome Atlas on the three tasks of the Tumor Proliferation Assessment Challenge (TUPAC). We obtained a performance within the top-3 best methods for most of the tasks of the challenge.Comment: Accepted to appear in IEEE Transactions on Medical Imagin