scholarly journals A Framework for Falsifiable Explanations of Machine Learning Models with an Application in Computational Pathology

2021 ◽  
Author(s):  
David Schuchmacher ◽  
Stephanie Schoerner ◽  
Claus Kuepper ◽  
Frederik Grosserueschkamp ◽  
Carlo Sternemann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Label Free ◽  
Learning Models ◽  
Intermediate Space ◽  
Black Boxes ◽  
Localize Tumor ◽  
Key Driver ◽  
Computational Pathology ◽  
Histological Staining ◽  
Machine Learning Models

In recent years, deep learning has been the key driver of breakthrough developments in computational pathology and other image based approaches that support medical diagnosis and treatment. The underlying neural networks as inherent black boxes lack transparency, and are often accompanied by approaches to explain their output. However, formally defining explainability has been a notorious unsolved riddle. Here, we introduce a hypothesis-based framework for falsifiable explanations of machine learning models. A falsifiable explanation is a hypothesis that connects an intermediate space induced by the model with the sample from which the data originate. We instantiate this framework in a computational pathology setting using label-free infrared microscopy. The intermediate space is an activation map, which is trained with an inductive bias to localize tumor. An explanation is constituted by hypothesizing that activation corresponds to tumor and associated structures, which we validate by histological staining as an independent secondary experiment.

Label‐Free and In Situ Identification of Cells via Combinational Machine Learning Models

Small Methods ◽  
2021 ◽  
pp. 2101405
Author(s):  
Yun‐fan Xue ◽  
Yang He ◽  
Jing Wang ◽  
Ke‐feng Ren ◽  
Pu Tian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Label Free ◽  
Learning Models ◽  
Machine Learning Models

Adversarial Attacks and Defenses

2021 ◽  
Vol 23 (1) ◽  
pp. 86-99
Author(s):  
Ninghao Liu ◽  
Mengnan Du ◽  
Ruocheng Guo ◽  
Huan Liu ◽  
Xia Hu
Keyword(s):  
Machine Learning ◽  
Deep Neural Networks ◽  
Wide Spectrum ◽  
Learning Models ◽  
Working Mechanism ◽  
Future Directions ◽  
Model Interpretation ◽  
Black Boxes ◽  
Attacks And Defenses ◽  
Machine Learning Models

Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. Techniques to protect models against adversarial input are called adversarial defense methods. Although many approaches have been proposed to study adversarial attacks and defenses in different scenarios, an intriguing and crucial challenge remains that how to really understand model vulnerability? Inspired by the saying that "if you know yourself and your enemy, you need not fear the battles", we may tackle the challenge above after interpreting machine learning models to open the black-boxes. The goal of model interpretation, or interpretable machine learning, is to extract human-understandable terms for the working mechanism of models. Recently, some approaches start incorporating interpretation into the exploration of adversarial attacks and defenses. Meanwhile, we also observe that many existing methods of adversarial attacks and defenses, although not explicitly claimed, can be understood from the perspective of interpretation. In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation. We categorize interpretation into two types, feature-level interpretation, and model-level interpretation. For each type of interpretation, we elaborate on how it could be used for adversarial attacks and defenses. We then briefly illustrate additional correlations between interpretation and adversaries. Finally, we discuss the challenges and future directions for tackling adversary issues with interpretation.

scholarly journals A survey of surveys on the use of visualization for interpreting machine learning models

2020 ◽  
Vol 19 (3) ◽  
pp. 207-233 ◽  
Author(s):  
Angelos Chatzimparmpas ◽  
Rafael M. Martins ◽  
Ilir Jusufi ◽  
Andreas Kerren
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Original Research ◽  
Learning Models ◽  
Visual Interpretation ◽  
Research Papers ◽  
Black Boxes ◽  
Internal Operations ◽  
Definition Of ◽  
Machine Learning Models

Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

scholarly journals Analysis of Conditions for Reliable Predictions by Moodle Machine Learning Models

2021 ◽  
Vol 16 (06) ◽  
pp. 106
Author(s):  
László Bognár ◽  
Tibor Fauszt ◽  
Gábor Zsolt Nagy
Keyword(s):  
Machine Learning ◽  
Splitting Method ◽  
False Alarms ◽  
Learning Models ◽  
Black Boxes ◽  
Statistics Course ◽  
Model Set ◽  
Set Up ◽  
The University ◽  
Machine Learning Models

In this paper the issue of bias-variance trade-off in building and operating Moodle Machine Learning (ML) models are discussed to avoid traps of get-ting unreliable predictions. Moodle is one of the world’s most popular open source Learning Management System (LMS) with millions of users. Although since Moodle 3.4 release it is possible to create ML models within the LMS system very few studies have been published so far about the conditions of its proper application. Using these models as black boxes hold serious risks to get unreliable predictions and false alarms. From a comprehensive study of differently built machine learning models elaborated at the University of Dunaújváros in Hungary, one specific issue is addressed here, namely the in-fluence of the size and the row-column ratio of the predictor matrix on the goodness of the predictions. In the so-called Time Splitting Method in Moo-dle Learning Analytics the effect of varying numbers of time splits and of predictors has also been studied to see their influence on the bias and the variance of the models. An Applied Statistics course is used to demonstrate the consequences of the different model set up.

scholarly journals Contextualized LORE for Fuzzy Attributes

2021 ◽  
Author(s):  
Najlaa Maaroof ◽  
Antonio Moreno ◽  
Mohammed Jabreel ◽  
Aida Valls
Keyword(s):  
Machine Learning ◽  
Contextual Information ◽  
Black Box ◽  
New Method ◽  
Learning Models ◽  
Box Models ◽  
Black Boxes ◽  
Linguistic Labels ◽  
Black Box Models ◽  
Machine Learning Models

Despite the broad adoption of Machine Learning models in many domains, they remain mostly black boxes. There is a pressing need to ensure Machine Learning models that are interpretable, so that designers and users can understand the reasons behind their predictions. In this work, we propose a new method called C-LORE-F to explain the decisions of fuzzy-based black box models. This new method uses some contextual information about the attributes as well as the knowledge of the fuzzy sets associated to the linguistic labels of the fuzzy attributes to provide actionable explanations. The experimental results on three datasets reveal the effectiveness of C-LORE-F when compared with the most relevant related works.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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