Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpreting Medical Image Classifiers by Optimization Based
  Counterfactual Impact Analysis   Clinical applicability of automated decision support systems depends on a
robust, well-understood classification interpretation. Artificial neural
networks while achieving class-leading scores fall short in this regard.
Therefore, numerous approaches have been proposed that map a salient region of
an image to a diagnostic classification. Utilizing heuristic methodology, like
blurring and noise, they tend to produce diffuse, sometimes misleading results,
hindering their general adoption. In this work we overcome these issues by
presenting a model agnostic saliency mapping framework tailored to medical
imaging. We replace heuristic techniques with a strong neighborhood conditioned
inpainting approach, which avoids anatomically implausible artefacts. We
formulate saliency attribution as a map-quality optimization task, enforcing
constrained and focused attributions. Experiments on public mammography data
show quantitatively and qualitatively more precise localization and clearer
conveying results than existing state-of-the-art methods.

15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas