M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation
Xiaoqi Zhao; Hongpeng Jia; Youwei Pang; Long Lv; Feng Tian; Lihe Zhang; Weibing Sun; Huchuan Lu · 2026 · Machine Intelligence Research
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate localization and blurred edges of lesions. To address this challenge, we propose a general multi-scale in multi-scale subtraction network (M 2 SNet) to finish diverse segmentati
Abstract by Xiaoqi Zhao; Hongpeng Jia; Youwei Pang; Long Lv; Feng Tian; Lihe Zhang; Weibing Sun; Huchuan Lu, Machine Intelligence Research (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s11633-026-1662-9