{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:59:04Z","timestamp":1781279944254,"version":"3.54.1"},"posted":{"date-parts":[[2024,11,6]]},"reference-count":0,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>We propose two attention functions that capture cross-channel statistical scaling regularities: Monofractal and Multifractal recalibration. We build an experimental framework centered around the U-Net [1] and show that these approaches, especially Multifractal recalibration, lead to substantial improvements over a baseline augmented with other attention functions that may also describe each channel in terms of higher-order statistics [2]-[4]. Our experiments cover three public datasets from diverse modalities: ISIC18 (dermoscopy) [5], Kvasir-SEG (endoscopy) [6], and BUSI (ultrasound) [7]. Additionally, we also study the dynamics of the Squeezeand-Excite attention layer [8] and our findings suggest that (a) excitation response does not get increasingly specialized with encoder depth in the U-Net due to its skip connections, and that its effectiveness may be linked to global statistics of their instance-variability. To the best of our knowledge, we present the first instance of end-to-end multifractal analysis for semantic segmentation.<\/jats:p>","DOI":"10.36227\/techrxiv.173091506.67735014\/v1","type":"posted-content","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T12:44:35Z","timestamp":1730897075000},"source":"Crossref","is-referenced-by-count":0,"title":["Monofractal and Multifractal Recalibration of Fully Convolutional Networks for Medical Image Segmentation"],"prefix":"10.36227","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1362-6136","authenticated-orcid":false,"given":"Miguel L.","family":"Martins","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel T.","family":"Coimbra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Renna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.techrxiv.org\/doi\/pdf\/10.36227\/techrxiv.173091506.67735014\/v1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:04:29Z","timestamp":1781276669000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techrxiv.org\/doi\/full\/10.36227\/techrxiv.173091506.67735014\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.36227\/techrxiv.173091506.67735014\/v1","relation":{},"subject":[],"published":{"date-parts":[[2024,11,6]]},"subtype":"preprint"}}