Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)

Breast Cancer (BC) is among women's most lethal health concerns. Early diagnosis can alleviate the mortality rate by helping patients make efficient treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become one the most lethal subtype of BC. According to the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO), the severity level of HER2 expression can be classified between 0 and 3+ range. HER2 can be detected effectively from immunohistochemical (IHC) and, hematoxylin \& eosin (HE) images of different classes such as 0, 1+, 2+, and 3+. An ensemble approach integrated with threshold filtered single instance evaluation (SIE) technique has been proposed in this study to diagnose BC from the multi-categorical expression of HER2 subtypes. Initially, DenseNet201 and Xception have been ensembled into a single classifier as feature extractors with an effective combination of global average pooling, dropout layer, dense layer with a swish activation function, and l2 regularizer, batch normalization, etc. After that, extracted features has been processed through single instance evaluation (SIE) to determine different confidence levels and adjust decision boundary among the imbalanced classes. This study has been conducted on the BC immunohistochemical (BCI) dataset, which is classified by pathologists into four stages of HER2 BC. This proposed approach known as DenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all other existing state-of-art models with an accuracy of 97.12\%, precision of 97.15\%, and recall of 97.68\% on H\&E data and, accuracy of 97.56\%, precision of 97.57\%, and recall of 98.00\% on IHC data respectively, maintaining momentous improvement. Finally, Grad-CAM and Guided Grad-CAM have been employed in this study to interpret, how TL-based model works on the histopathology dataset and make decisions from the data

Real-time recommendation algorithms for crowdsourcing systems

Crowdsourcing has become a promising paradigm for solving tasks that are beyond the capabilities of machines alone via outsourcing tasks to online crowds of people. Both requesters and workers in crowdsourcing systems confront a flood of data coming along with the vast amount of tasks. Fast, on-the-fly recommendation of tasks to workers and workers to requesters is becoming critical for crowdsourcing systems. Traditional recommendation algorithms such as collaborative filtering no longer work satisfactorily because of the unprecedented data flow and the on-the-fly nature of the tasks in crowdsourcing systems. A pressing need for real-time recommendations has emerged in crowdsourcing systems: on the one hand, workers want effective recommendation of the top-k most suitable tasks with regard to their skills and preferences, and on the other hand, requesters want reliable recommendation of the top-k best workers for their tasks in terms of workers’ qualifications and accountability. In this article, we propose two real-time recommendation algorithms for crowdsourcing systems: (1) TOP-K-T that computes the top-k most suitable tasks for a given worker and (2) TOP-K-W that computes the top-k best workers to a requester with regard to a given task. Experimental study has shown the efficacy of both algorithms

Advances in Cloud Computing

International audienceCloud Computing [1, 3] represents a major paradigm shift incomputing and information technology strategy. The "Cloud"is a natural evolution of distributed computing and ofwidespread adoption of the virtualization technology and SOA.In Cloud Computing, IT-related capabilities and resources areprovisioned as services, via the Internet and with the essentialcharacteristics such as on-demand, elasticity, metered services,and rapid provision (without requiring possession of detailedknowledge of the underlying technology). The InternationalJournal of Computers and Their Applications (IJCA) has thusscheduled this special issue in response to the fast developmentand increased application of Cloud Computing

Rule-Based Generation of Logical Query Plans with Controlled Complexity

Rule--based query optimizers are recognized as particularly valuable for extensible and object--oriented database management systems by providing a high flexibility in adapting query optimization strategies to nonstandard application needs. On the other hand rule--based optimizers areproblematic with regard to run--time behavior for more complex queries as the generation of query plans based on a declarative rule base tends to be difficult to control. In this paper we show that this is not a fundamental problem of rule--based optimizers, but rather a question of careful design of the rule system. We exemplify this for one fundamental optimization problem, namely join enumeration for linear queries.There,arule--basedoptimizationstrategycanachievethetheoreticallyoptimalcomplexity. The design principles used to achieve this have been derived from and are used in the design of the VODAK query optimizer developed at GMD--IPSI

Query optimization in XML structured-document databases

While the information published in the form of XML-compliant documents keeps fast mounting up, efficient and effective query processing and optimization for XML have now become more important than ever. This article reports our recent advances in XML structureddocument query optimization. In this article, we elaborate on a novel approach and the techniques developed for XML query optimization. Our approach performs heuristic-based algebraic transformations on XPath queries, represented as PAT algebraic expressions, to achieve query optimization. This article first presents a comprehensive set of general equivalences with regard to XML documents and XML queries. Based on these equivalences, we developed a large set of deterministic algebraic transformation rules for XML query optimization. Our approach is unique, in that it performs exclusively deterministic transformations on queries for fast optimization. The deterministic nature of the proposed approach straightforwardly renders high optimization efficiency and simplicity in implementation. Our approach is a logical-level one, which is independent of any particular storage model. Therefore, the optimizers developed based on our approach can be easily adapted to a broad range of XML data/information servers to achieve fast query optimization. Experimental study confirms the validity and effectiveness of the proposed approach