With the increase in use of machine learning classifiers in several fields, providing human- understandable explanation of their outputs has become an imperative. It is essential to generate trust for day-to-day tasks, especially in the sensible domains as medical imaging. Although many works have addressed this problem by generating visual explanation maps, they often provide noisy and inaccurate results forcing heuristic regularization unrelated to the classifier in question. In this paper, we propose a general perspective of the visual explanation problem overcoming these limitations. We show that visual explanation can be produced as the difference between two generated images obtained via two specific conditional generative models. Both generative models are trained using the classifier to explain and a database to enforce the following properties: (i) All images generated by the first generator are classified similarly to the input image, whereas the second generator’s outputs are classified oppositely. (ii) All generated images belong to the distribution of real images. (iii) The distances between the input image and the corresponding generated images are minimal so that the difference between the generated elements only reveals relevant information for the studied classifier. Using symmetrical and cyclic constraints, we present two different approximations and implementations of the general formulation. Experimentally, we demonstrate significant improvements with respect to the state-of-the-art on three different public data sets. In particular, the localization of regions influencing the classifier is consistent with human annotations.

Explaining decisions of black-box classifiers is paramount in sensitive domains such as medical imaging since clinicians confidence is necessary for adoption. Various explanation approaches have been proposed, among which perturbation based approaches are very promising. Within this class of methods, we leverage a learning framework to produce our visual explanations method. From a given classifier, we train two generators to produce from an input image the so called similar and adversarial images. The similar image shall be classified as the input image whereas the adversarial shall not. Visual explanation is built as the difference between these two generated images. Using metrics from the literature, our method outperforms state-of-the-art approaches. The proposed approach is model-agnostic and has a low computation burden at prediction time. Thus, it is adapted for real-time systems. Finally, we show that random geometric augmentations applied to the original image play a regularization role that improves several previously proposed explanation methods. We validate our approach on a large chest X-ray database.

In the context of variational image segmentation, we propose a new finite-dimensional implicit surface representation. The key idea is to span a subset of implicit functions with linear combinations of spatially-localized kernels that follow image features. This is achieved by replacing the Euclidean distance in conventional Radial Basis Functions with non-Euclidean, image-dependent distances. For the minimization of an objective region-based criterion, this representation yields more accurate results with fewer control points than its Euclidean counterpart. If the user positions these control points, the non-Euclidean distance enables to further specify our localized kernels for a target object in the image. Moreover, an intuitive control of the result of the segmentation is obtained by casting inside/outside labels as linear inequality constraints. Finally, we discuss several algorithmic aspects needed for a responsive interactive workflow. We have applied this framework to 3D medical imaging and built a real-time prototype with which the segmentation of whole organs is only a few clicks away.

We describe a novel variational segmentation algorithm designed to split an image in two regions based on their intensity distributions. A functional is proposed to integrate the unknown probability density functions of both regions within the optimization process. The method simultaneously performs segmentation and non-parametric density estimation. It does not make any assumption on the underlying distributions, hence it is flexible and can be applied to a wide range of applications. Although a boundary evolution scheme may be used to minimize the functional, we choose to consider an alternative formulation with a membership function. The latter has the advantage of being convex in each variable, so that the minimization is faster and less sensitive to initial conditions. Finally, to improve the accuracy and the robustness to low-frequency artifacts, we present an extension for the more general case of local space-varying probability densities. The approach readily extends to vectorial images and 3D volumes, and we show several results on synthetic and photographic images, as well as on 3D medical data.

In this paper we introduce a novel edge-based, implicit approach for single object segmentation in 3D images. From a couple of curves, traced by the user on the object to be segmented, we implicitly generate a surface that contains the set of minimal paths joining them. These paths are minimal with respect to a cost function that takes lower values on the object to be extracted, hence they are within a small distance from it. Unlike other variational approaches, our method is not concerned with local minima traps. Our algorithm has been successfully applied to 3D medical images and synthetic images.