Co-evolution, opportunity seeking and institutional change: Entrepreneurship and the Indian telecommunications industry 1923-2009

"This is an Author's Original Manuscript of an article submitted for consideration in Business History [copyright Taylor & Francis]; Business History is available online at http://www.tandfonline.com/." 10.1080/00076791.2012.687538In this paper, we demonstrate the importance for entrepreneurship of historical contexts and processes, and the co-evolution of institutions, practices, discourses and cultural norms. Drawing on discourse and institutional theories, we develop a model of the entrepreneurial field, and apply this in analysing the rise to global prominence of the Indian telecommunications industry. We draw on entrepreneurial life histories to show how various discourses and discursive processes ultimately worked to generate change and the creation of new business opportunities. We propose that entrepreneurship involves more than individual acts of business creation, but also implies collective endeavours to shape the future direction of the entrepreneurial field

Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty

Bayesian Neural Nets (BNN) are increasingly used for robust organ auto-contouring. Uncertainty heatmaps extracted from BNNs have been shown to correspond to inaccurate regions. To help speed up the mandatory quality assessment (QA) of contours in radiotherapy, these heatmaps could be used as stimuli to direct visual attention of clinicians to potential inaccuracies. In practice, this is non-trivial to achieve since many accurate regions also exhibit uncertainty. To influence the output uncertainty of a BNN, we propose a modified accuracy-versus-uncertainty (AvU) metric as an additional objective during model training that penalizes both accurate regions exhibiting uncertainty as well as inaccurate regions exhibiting certainty. For evaluation, we use an uncertainty-ROC curve that can help differentiate between Bayesian models by comparing the probability of uncertainty in inaccurate versus accurate regions. We train and evaluate a FlipOut BNN model on the MICCAI2015 Head and Neck Segmentation challenge dataset and on the DeepMind-TCIA dataset, and observed an increase in the AUC of uncertainty-ROC curves by 5.6% and 5.9%, respectively, when using the AvU objective. The AvU objective primarily reduced false positives regions (uncertain and accurate), drawing less visual attention to these regions, thereby potentially improving the speed of error detection.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer Graphics and VisualisationPattern Recognition and Bioinformatic

Comparing Bayesian models for organ contouring in head and neck radiotherapy

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Bayesian models and their associated uncertainty, can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure – expected calibration error (ECE) and a qualitative measure – region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low ECE to be considered trustworthy. However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions. Such behaviour could direct visual attention of expert users to potentially inaccurate regions, leading to a speed-up in the QA process. Using R-AvU graphs, we qualitatively compare the behaviour of different models in accurate and inaccurate regions. Experiments are conducted on the MICCAI2015 Head and Neck Segmentation Challenge and on the DeepMindTCIA CT dataset using three models: DropOut-DICE, Dropout-CE (Cross Entropy) and FlipOut-CE. Quantitative results show that DropOut-DICE has the highest ECE, while Dropout-CE and FlipOut-CE have the lowest ECE. To better understand the difference between DropOut-CE and FlipOut-CE, we use the R-AvU graph which shows that FlipOut-CE has better uncertainty coverage in inaccurate regions than DropOut-CE. Such a combination of quantitative and qualitative metrics explores a new approach that helps to select which model can be deployed as a QA tool in clinical settings.<br/

Comparing Bayesian models for organ contouring in head and neck radiotherapy

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Bayesian models and their associated uncertainty, can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure – expected calibration error (ECE) and a qualitative measure – region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low ECE to be considered trustworthy. However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions. Such behaviour could direct visual attention of expert users to potentially inaccurate regions, leading to a speed-up in the QA process. Using R-AvU graphs, we qualitatively compare the behaviour of different models in accurate and inaccurate regions. Experiments are conducted on the MICCAI2015 Head and Neck Segmentation Challenge and on the DeepMindTCIA CT dataset using three models: DropOut-DICE, Dropout-CE (Cross Entropy) and FlipOut-CE. Quantitative results show that DropOut-DICE has the highest ECE, while Dropout-CE and FlipOut-CE have the lowest ECE. To better understand the difference between DropOut-CE and FlipOut-CE, we use the R-AvU graph which shows that FlipOut-CE has better uncertainty coverage in inaccurate regions than DropOut-CE. Such a combination of quantitative and qualitative metrics explores a new approach that helps to select which model can be deployed as a QA tool in clinical settings.Computer Graphics and VisualisationHuman Information Communication Desig

Comparing Bayesian models for organ contouring in head and neck radiotherapy

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Bayesian models and their associated uncertainty, can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure – expected calibration error (ECE) and a qualitative measure – region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low ECE to be considered trustworthy. However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions. Such behaviour could direct visual attention of expert users to potentially inaccurate regions, leading to a speed-up in the QA process. Using R-AvU graphs, we qualitatively compare the behaviour of different models in accurate and inaccurate regions. Experiments are conducted on the MICCAI2015 Head and Neck Segmentation Challenge and on the DeepMindTCIA CT dataset using three models: DropOut-DICE, Dropout-CE (Cross Entropy) and FlipOut-CE. Quantitative results show that DropOut-DICE has the highest ECE, while Dropout-CE and FlipOut-CE have the lowest ECE. To better understand the difference between DropOut-CE and FlipOut-CE, we use the R-AvU graph which shows that FlipOut-CE has better uncertainty coverage in inaccurate regions than DropOut-CE. Such a combination of quantitative and qualitative metrics explores a new approach that helps to select which model can be deployed as a QA tool in clinical settings.Computer Graphics and VisualisationHuman Information Communication Desig

Toxicities of systemic agents in squamous cell carcinoma of the head and neck (SCCHN); A new perspective in the era of immunotherapy

Item does not contain fulltextSquamous cell carcinoma of the head and neck (SCCHN) is a difficult to treat malignancy and represents the seventh most common cancer worldwide. Systemic therapy has a critical role in the treatment of locally advanced and recurrent/metastatic disease. Cytotoxic chemotherapy has been primarily used along with radiation and surgery, with cisplatin being the standard of care choice of therapy. When contraindications to cisplatin exist, other agents such as carboplatin, taxanes, 5-fluorouracil, and cetuximab are used. Similarly, in the advanced or metastatic setting, platinum agents, taxanes and cetuximab have been predominantly utilized. With the recent approval of novel agents such as pembrolizumab and nivolumab, and their distinct toxicity profiles, an understanding of the potential sequelae of the different systemic agents is essential to the careful selection of agents in the advanced disease setting. Going forward, choosing novel agents will be weighed against traditional chemotherapy, and understanding the toxicities at stake is critical in this process. In addition to providing an overview of the toxicity profile of the different systemic agents, we also provide a perspective into the future of SCCHN treatment

Towards fast human-centred contouring workflows for adaptive external beam radiotherapy

Delineation of tumours and organs-at-risk permits detecting and correcting changes in the patients' anatomy throughout the treatment, making it a core step of adaptive external beam radiotherapy. Although auto-contouring technologies have sped up this process, the time needed to perform the quality assessment of the generated contours remains a bottleneck, taking clinicians between several minutes and an hour to complete. The authors of this article conducted several interviews and an observational study at two treatment centres in the Netherlands to identify challenges and opportunities for speeding up the delineation process in adaptive therapies. The study revealed three contextual variables that influence contouring performance: usable additional information, applicable domain-specific knowledge, and available editing capabilities in contouring software. In practice, clinicians leverage these variables to accelerate contouring in two ways. First, they use domain-specific knowledge and relevant clinical features such as the proximity of the organs-at-risk to the tumour to enable targeted inspection of the delineation. Second, clinicians modulate editing precision depending on the effect they anticipate the edit will have on the patient outcome. By implementing these acceleration strategies in guidelines and contouring tools, developers and workflow builders could increase contouring efficiency and consistency without affecting the patient outcome.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer Graphics and VisualisationPattern Recognition and BioinformaticsHuman Information Communication Desig

Novel Immunotherapeutic Approaches to Treating HPV-Related Head and Neck Cancer.

Head and neck cancer (HNC) is the seventh most common malignancy, with oropharyngeal squamous cell carcinoma (OPSCC) accounting for a majority of cases in the western world. While HNC accounts for only 5% of all cancers in the United States, the incidence of a subset of OPSCC caused by human papillomavirus (HPV) is increasing rapidly. The treatment for OPSCC is multifaceted, with a recently emerging focus on immunotherapeutic approaches. With the increased incidence of HPV-related OPSCC and the approval of immunotherapy in the management of recurrent and metastatic HNC, there has been rising interest in exploring the role of immunotherapy in the treatment of HPV-related OPSCC specifically. The immune microenvironment in HPV-related disease is distinct from that in HPV-negative OPSCC, which has prompted further research into various immunotherapeutics. This review focuses on HPV-related OPSCC, its immune characteristics, and current challenges and future opportunities for immunotherapeutic applications in this virus-driven cancer