Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability. They use to work as intuition machines with high statistical confidence but unable to give interpretable explanations about the reported results. The vast amount of parameters of these models make difficult to infer a rationale interpretation from them. In this paper we present a diabetic retinopathy interpretable classifier able to classify retine images into the different levels of disease severity and of explaining its results by assigning a score for every point in the hidden and input space, evaluating its contribution to the final classification in a linear way. The generated visual maps can be interpreted by an expert in order to compare its own knowledge with the interpretation given by the model.
In @cite_7 the authors split the total score of a classification into individual that act as a positive or negative contributions to the final result.
@cite_7
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Evaluating text fragments for positive and negative subjective expressions and their strength can be important in applications such as single- or multi- document summarization, document ranking, data mining, etc. This paper looks at a simplified version of the problem: classifying online product reviews into positive and negative classes. We discuss a series of experiments with different machine learning algorithms in order to experimentally evaluate various trade-offs, using approximately 100K product reviews from the web. There were 20 scholars interviewed about their citation motives in recently published articles. Their 437 citations were scaled along 1 or more of the following 7 citer motives: currency, negative credit, operational information, persuasiveness, positive credit, reader alert, and social consensus. The majority (70.7 ) of the references were attributed to more than 1 motive. Analysis of the clustering of the citer motives showed 3 groupings: (1) persuasiveness, positive credit, currency, and social consensus, (2) negative credit, and (3) reader alert and operational information. Negative credit references were often found to be used with a countervailing positive credit, currency, or social consensus reference. This is considered to be empirical evidence of MacRoberts and MacRoberts' [8] hypothesis that scholars dissemble when giving negative references. © 1986 John Wiley & Sons, Inc. Performance metrics for binary classification are designed to capture tradeoffs between four fundamental population quantities: true positives, false positives, true negatives and false negatives. Despite significant interest from theoretical and applied communities, little is known about either optimal classifiers or consistent algorithms for optimizing binary classification performance metrics beyond a few special cases. We consider a fairly large family of performance metrics given by ratios of linear combinations of the four fundamental population quantities. This family includes many well known binary classification metrics such as classification accuracy, AM measure, F-measure and the Jaccard similarity coefficient as special cases. Our analysis identifies the optimal classifiers as the sign of the thresholded conditional probability of the positive class, with a performance metric-dependent threshold. The optimal threshold can be constructed using simple plug-in estimators when the performance metric is a linear combination of the population quantities, but alternative techniques are required for the general case. We propose two algorithms for estimating the optimal classifiers, and prove their statistical consistency. Both algorithms are straightforward modifications of standard approaches to address the key challenge of optimal threshold selection, thus are simple to implement in practice. The first algorithm combines a plug-in estimate of the conditional probability of the positive class with optimal threshold selection. The second algorithm leverages recent work on calibrated asymmetric surrogate losses to construct candidate classifiers. We present empirical comparisons between these algorithms on benchmark datasets. Statistical aggregation is often used to combine multiple opinions within a group. Such aggregates outperform individuals, including experts, in various prediction and estimation tasks. This result is attributed to the “wisdom of crowds.” We seek to improve the quality of such aggregates by eliminating poorly performing individuals from the crowd. We propose a new measure of contribution to assess the judges' performance relative to the group and use positive contributors to build a weighting model for aggregating forecasts. In Study 1, we analyze 1,233 judges forecasting almost 200 current events to illustrate the superiority of our model over unweighted models and models weighted by measures of absolute performance. In Study 2, we replicate our findings by using economic forecasts from the European Central Bank and show how the method can be used to identify smaller crowds of the top positive contributors. We show that the model derives its power from identifying experts who consistently outperform the crowd. Data, as supplemental material, are available at http: dx.doi.org 10.1287 mnsc.2014.1909 . This paper was accepted by James Smith, decision analysis.