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多核ゲート付きデコーダーアダプターによるクロスセンター環境下での頑健なマルチタスク甲状腺超音波解析

arXiv cs.CV / 2026/3/11

Ideas & Deep AnalysisModels & Research

要点

  • 本論文は、甲状腺超音波解析の自動化における課題に取り組み、グローバルな幾何学に基づくセグメンテーションと局所的なテクスチャに基づく悪性評価をクロスセンター領域シフトの下で両立させる点を扱っている。
  • 畳み込みニューラルネットワーク(CNN)と医療用ビジョントランスフォーマー(ViT)が補完的な強みを持つことを指摘し、ViTは幾何学的セグメンテーションに優れ、CNNはドメインシフト下で悪性を示すテクスチャ情報を保持することを示した。
  • マルチタスク学習で共有バックボーンからのネガティブトランスファーを軽減するために、マルチスケールかつセマンティックな文脈を用いてアーティファクトに影響されやすい特徴を選択的にゲーティングするMulti-Kernel Gated Adapters(MKGA)とその残差バリアント(ResMKGA)を提案する。
  • 2つの超音波ベンチマークでの実験により、クロスセンター状況下での堅牢性が向上し、ドメイン外セグメンテーション性能および臨床診断精度が改善、特にCNNバックボーンで顕著な成果が得られた。
  • 将来的にコードおよびモデルを公開し、甲状腺超音波解析の医療画像AIのさらなる研究と実用化を支援する予定である。

Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.08906 (cs)
[Submitted on 9 Mar 2026]

Title:Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift

View a PDF of the paper titled Multi-Kernel Gated Decoder Adapters for Robust Multi-Task Thyroid Ultrasound under Cross-Center Shift, by Maziar Sabouri and 2 other authors
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Abstract:Thyroid ultrasound (US) automation couples two competing requirements: global, geometry-driven reasoning for nodule delineation and local, texture-driven reasoning for malignancy risk assessment. Under cross-center domain shift, these cues degrade asymmetrically, yet most multi-task pipelines rely on a single shared backbone, often inducing negative transfer. In this paper, we characterize this interference across CNN (ResNet34) and medical ViT (MedSAM) backbones, and observe a consistent trend: ViTs transfer geometric priors that benefit segmentation, whereas CNNs more reliably preserve texture cues for malignancy discrimination under strong shift and artifacts. Motivated by this failure mode, we propose a lightweight family of decoder-side adapters, the Multi-Kernel Gated Adapter (MKGA) and a residual variant (ResMKGA), which refine multi-scale skip features using complementary receptive fields and apply semantic, context-conditioned gating to suppress artifact-prone content before fusion. Across two US benchmarks, the proposed adapters improve cross-center robustness: they strengthen out-of-domain segmentation and, in the CNN setting, yield clear gains in clinical TI-RADS diagnostic accuracy compared to standard multi-task baselines. Code and models will be released.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
Cite as: arXiv:2603.08906 [cs.CV]
  (or arXiv:2603.08906v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.08906
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arXiv-issued DOI via DataCite

Submission history

From: Maziar Sabouri [view email]
[v1] Mon, 9 Mar 2026 20:18:35 UTC (7,797 KB)
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