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Accurate diagnoses often require the detection and combination of complex factors present in the input. Explanation is therefore particularly challenging, but also necessary due to the high stakes involved. Most existing explanation methods fall short in isolating distributed and overlapping features such as colors and textures. This paper introduces <jats:italic>Spectral Occlusion<\/jats:italic> (<jats:italic>S-Occ<\/jats:italic>), a method designed to address this limitation, providing multiple additional perspectives to the explanation of complex decisions. Beyond the conventional highlighting of spatial regions, <jats:italic>S-Occ<\/jats:italic> makes use of spectral manipulation to indicate dispersed image features such as colors and textures. Different visualizations offer an additional, nuanced insight into the model\u2019s decision-making, resulting in a more holistic representation of the contributing factors. This can help to facilitate the explainability of complex systems. The method is evaluated quantitatively and qualitatively on real-world skin lesion analysis. <jats:italic>S-Occ<\/jats:italic> outperforms established methods by an average of 0.38 in explanation <jats:italic>Sensitivity<\/jats:italic>, demonstrating its ability to complement spatial attribution methods by facilitating the highlighting of non-trivial, decision-relevant factors. The method\u2019s potential impact spans various high-stakes domains, with particular relevance in medical fields like dermatology and ophthalmology, where nuanced insights are imperative for trustworthy decision-making.<\/jats:p>","DOI":"10.1007\/978-3-032-08333-3_8","type":"book-chapter","created":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T05:22:49Z","timestamp":1760764969000},"page":"159-183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spectral Occlusion - Attribution Beyond Spatial Relevance Heatmaps"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8222-7900","authenticated-orcid":false,"given":"Fabian","family":"Schmeisser","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1473-4745","authenticated-orcid":false,"given":"Adriano","family":"Lucieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-8255","authenticated-orcid":false,"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-6520","authenticated-orcid":false,"given":"Sheraz","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,19]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Abello, A.A., Hirata, R., Wang, Z.: Dissecting the high-frequency bias in convolutional neural networks. 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