Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A more particular shortcoming of deep models is the ignorance toward search functionality. The former affects every model, the latter only search and matching. Due to the lack of ranking-based learning, researchers must train models based on the classification error and then use the resultant embedding for image search purposes. Moreover, deep models appear to be prone to internal bias even if using a large image repository of various hospitals. This paper proposes two novel ideas to improve image search performance. First, we use a ranking loss function to guide feature extraction toward the matching-oriented nature of the search. By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label. Second, we introduce the concept of sequestering learning to enhance the generalization of feature extraction. By excluding the images of the input hospital from the matched outputs, i.e., sequestering the input domain, the institutional bias is reduced. The proposed ideas are implemented and validated through the largest public dataset of whole slide images. The experiments demonstrate superior results compare to the-state-of-art.Comment: Under Review for publicatio

Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images

One of the main obstacles of adopting digital pathology is the challenge of efficient processing of hyperdimensional digitized biopsy samples, called whole slide images (WSIs). Exploiting deep learning and introducing compact WSI representations are urgently needed to accelerate image analysis and facilitate the visualization and interpretability of pathology results in a postpandemic world. In this paper, we introduce a new evolutionary approach for WSI representation based on large-scale multi-objective optimization (LSMOP) of deep embeddings. We start with patch-based sampling to feed KimiaNet , a histopathology-specialized deep network, and to extract a multitude of feature vectors. Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features. In the second stage, the frequent features histogram (FFH), a novel WSI representation, is constructed by multiple runs of coarse LSMOP. Fine evolutionary feature selection is then applied to find a compact (short-length) feature vector based on the FFH and contributes to a more robust deep-learning approach to digital pathology supported by the stochastic power of evolutionary algorithms. We validate the proposed schemes using The Cancer Genome Atlas (TCGA) images in terms of WSI representation, classification accuracy, and feature quality. Furthermore, a novel decision space for multicriteria decision making in the LSMOP field is introduced. Finally, a patch-level visualization approach is proposed to increase the interpretability of deep features. The proposed evolutionary algorithm finds a very compact feature vector to represent a WSI (almost 14,000 times smaller than the original feature vectors) with 8% higher accuracy compared to the codes provided by the state-of-the-art methods

Evaluation of photoionization detector performance for measuring the airborne toluene

Background and aims: In the field of chemical agents at workplaces, traditional measurement method for assessing the volatile organic compounds (VOCs) concentration is using a gas chromatograph generally equipped with a flame ionization detector (GC-FID). However, there are some limitations in working with this equipment including equipment accessibility, necessity of highly trained operators, and the high cost of sample analysis. The aim of this study was to evaluate the performance of photoionization detector (PID) as a substitution for GC-FID in the measurement of toluene as a representative of the VOCs in experimental studies. Methods: This study was carried out by an experimental set up for generating toluene known concentrations at 5, 20, 50, 100, 200, 500 and 1000 ppm with relative humidity 13 ±2. The concentration values were measured with PID as well as the National Institute of Occupational Safety and Health (NIOSH) 1501 reference method and results were compared. Results: The results showed a significant difference between the two methods at concentrations higher than 50 ppm while there was no significant difference at 5 ppm and 20 ppm. The correlation coefficient of the toluene concentrations at 5 to 1000 ppm was 0.999. The correction factor for the PID was 1.05 at the studied concentration range. Conclusion: Although the results presented by PID were different from those extracted from the NIOSH reference method, the response was linear. Thus, in studies of measuring airborne concentrations of toluene using this type of detector; the reading values must be corrected by the calculated correction factor

Structural Modeling of Safety Performance in Construction Industry

Background: With rapid economic development and industrialization, the construction industry continues to rank among the most hazardous industries in the world. Therefore, construction safety is always a significant concern for both practitioners and researchers. The objective of this study was to create a structural modeling of components that influence the safety performance in construction projects. Methods: We followed a two-stage Structural Equation Model based on a questionnaire study (n=230). In the first stage, we applied the Structural Equation Model to the proposed model to test the validity of the observed variables of each latent variable. In the next stage, we modified the proposed model. The LISREL 8.8 software was used to conduct the analysis of the structural model. Results: A good-fit structural model (Goodness of Fit Index=0.92; Root Mean Square Residual=0.04; Root Mean Square Error of Approximation=0.04; Comparative Fit Index=0.98; Normalized Fit Index=0.96) indicated that social and organizational constructs influence safety performance via the general component of the safety climate. Conclusion: The new structural model can be used to provide better understanding of the links between safety performance indicators and contributing components, and make stronger recommendations for effective intervention in construction projects

Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously; for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search methods, for training neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the weights. In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method, in some cases, outperforms the gradient-based approach, particularly, in situations with insufficient labeled training data. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such as SGD or Adam. In this paper we introduce an alternative method for training ANN.Comment: 7 pages, 9 figure

Isolated Persian/Arabic handwriting characters: Derivative projection profile features, implemented on GPUs

For many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. However, an issue that has rarely been studied is the speed of these methods. Considering the computer hardware limitations, it is necessary for these methods to run in high speed. One of the methods to increase the processing speed is to use the computer parallel processing power. This paper introduces one of the best feature extraction methods for the handwritten recognition, called DPP (Derivative Projection Profile), which is employed for isolated Persian handwritten recognition. In addition to achieving good results, this (computationally) light feature can easily be processed. Moreover, Hamming Neural Network is used to classify this system. To increase the speed, some part of the recognition method is executed on GPU (graphic processing unit) cores implemented by CUDA platform. HADAF database (Biggest isolated Persian character database) is utilized to evaluate the system. The results show 94.5% accuracy. We also achieved about 5.5 times speed-up using GPU

Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers

This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors

Factors influencing unsafe behaviors and accidents on construction sites: A review

Objective. Construction is a hazardous occupation due to the unique nature of activities involved and the repetitiveness of several field behaviors. The aim of this methodological and theoretical review is to explore the empirical factors influencing unsafe behaviors and accidents on construction sites. Methods. In this work, results and findings from 56 related previous studies were investigated. These studies were categorized based on their design, type, methods of data collection, analytical methods, variables, and key findings. A qualitative content analysis procedure was used to extract variables, themes, and factors. In addition, all studies were reviewed to determine the quality rating and to evaluate the strength of provided evidence. Results. The content analysis identified 8 main categories: (a) society, (b) organization, (c) project management, (d) supervision, (e) contractor, (f) site condition, (g) work group, and (h) individual characteristics. The review highlighted the importance of more distal factors, e.g., society and organization, and project management, that may contribute to reducing the likelihood of unsafe behaviors and accidents through the promotion of site condition and individual features (as proximal factors). Conclusion. Further research is necessary to provide a better understanding of the links between unsafe behavior theories and empirical findings, challenge theoretical assumptions, develop new applied theories, and make stronger recommendations

Is Paromomycin an Effective and Safe Treatment against Cutaneous Leishmaniasis? A Meta-Analysis of 14 Randomized Controlled Trials

Millions of people worldwide are suffering from cutaneous leishmaniasis that is caused by parasites of the genus Leishmania. Although pentavalent antimony compounds are the treatment of choice, their use is limited by high cost, poor compliance, and systemic toxicity. Paromomycin was developed to overcome such limitations. However, there is no consensus on its efficacy. This meta-analysis assessed the efficacy and safety of paromomycin compared with placebo and pentavalent antimony compounds. Fourteen randomized controlled trials, including 1,221 patients, met our selection criteria. Topical paromomycin appeared to have therapeutic activity against the old world and new world cutaneous leishmaniasis, with increased local reactions, when combined with methylbenzethonium chloride. Topical paromomycin was not significantly different from intralesional pentavalent antimony compounds in treating the old world form, whereas it was inferior to parenteral pentavalent antimony compounds in treating the new world form. However, a similar efficacy was found between parenteral paromomycin and pentavalent antimony compounds in treating the new world form. Fewer systemic side effects were observed with topical and parenteral paromomycin than pentavalent antimony compounds. These results suggest that topical paromomycin with methylbenzethonium chloride could be a therapeutic alternative to pentavalent antimony compounds for selected cases of the old world cutaneous leishmaniasis

WR279,396, a Third Generation Aminoglycoside Ointment for the Treatment of Leishmania major Cutaneous Leishmaniasis: A Phase 2, Randomized, Double Blind, Placebo Controlled Study

Cutaneous leishmaniasis is due to a small parasite (Leishmania) that creates disfiguring sores, and affects more than one million persons (mainly children) each year. Treating lesions with a cream—instead of with injections as currently done—would greatly improve the well-being of affected patients. No cream formulation that would be efficient and would not create important skin irritation has been identified yet. Here, we tested a new cream formulation (WR279,396) containing paromomycin and gentamicin, two members of a well-known family of antibacterial antibiotics (aminoglycosides). Injectable paromomycin is efficient in other forms of the disease (visceral leishmaniasis). This was a carefully monitored study (phase 2) involving mainly children in Tunisia and France. The cream was applied twice a day for 20 days. The proportion of patients treated with the paromomycin-containing cream (active formulation) that cured (94%) was higher than that observed (71%) in patients treated with a cream that did not contain the active product (placebo formulation). Local irritation affected less than one-third of the patients and was usually mild. This new cream formulation was safe and effective in treating cutaneous leishmaniasis, thereby providing a new, simple, easily applicable, and inexpensive treatment for this neglected disease