BTI-Net: Bidirectional Decoder-Level Task Interaction via Uncertainty-Aware Gating for Multi-Task Medical Image Analysis

• URL: http://arxiv.org/abs/2606.29102v1
• Date: Sat, 27 Jun 2026 22:37:26 GMT
• Title: BTI-Net: Bidirectional Decoder-Level Task Interaction via Uncertainty-Aware Gating for Multi-Task Medical Image Analysis
• Abstract summary: BTI-Net establishes bidirectional communication at every decoder level through two parallel pathways via Task Interaction Modules.<n>Uncertainty Proxy Attention gates each TIM output per instance and per level using three signals that capture cross-task alignment, scene complexity, and prediction confidence.<n>Experiments on three medical benchmarks spanning ultrasound, dermoscopy, and brain MRI demonstrate consistent improvements in segmentation IoU and classification accuracy over both encoder-sharing and decoder-interaction baselines.
• Score: 2.8218290601001854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Jointly learning to segment and classify medical images demands cross-task synergy, yet encoder-sharing architectures limit decoder reconstruction to task-private representations, permanently discarding the boundary cues and semantic priors each branch could supply to the other. This work introduces BTI-Net, which establishes bidirectional communication at every decoder level through two parallel pathways via Task Interaction Modules (TIM). Spatial boundary context is gated into the classification branch, while global semantic priors multiplicatively modulate the decoder, with refined features propagating progressively from coarse semantics to fine boundary detail across all four decoder resolutions. Since cross-task interaction is not equally reliable for every input, Uncertainty Proxy Attention (UPA) gates each TIM output per instance and per level using three signals that capture cross-task alignment, scene complexity, and prediction confidence, without external annotations or additional inference passes. Experiments on three medical benchmarks spanning ultrasound, dermoscopy, and brain MRI demonstrate consistent improvements in segmentation IoU and classification accuracy over both encoder-sharing and decoder-interaction baselines. Ablation confirms adaptive gating contributes +2.36 IoU over fixed bidirectional interaction, and classification accuracy improves by up to +2.26 points over the strongest multi-task baseline. UPA's uncertainty proxies serve as reliable single-pass task-failure signals without the overhead of stochastic sampling. Code: https://github.com/C-loud-Nine/BTI-Net_MTL

Related papers

• DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance [17.49886552219562]
  DifferSeg is a simple yet general multimodal binary segmentation framework.<n>It employs learnable differential operators to adaptively align multimodal features and enhance their complementarity.<n>It consistently surpasses 67 state-of-the-art methods across 29 public datasets.
  arXiv  Detail & Related papers  (2026-06-08T01:10:36Z)
• GAFSV-Net: A Vision Framework for Online Signature Verification [6.4768782466318795]
  ImageSV-Net represents each signature as a six-channel asymmetric Gramian Angular Field imageSV-Net.<n>A dual-branch ConvNeXt-Tiny encoder processes GASF and GADF independently.<n>We evaluate on DeepSignDB and BiosecurID, outperforming all sequence-based baselines trained under identical objectives.
  arXiv  Detail & Related papers  (2026-04-30T18:21:07Z)
• WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference [56.297697169678095]
  WISV (Wireless-Informed Semantic Verification) is a novel distributed speculative decoding framework.<n>We show that WISV achieves up to a 60.8% increase in accepted length, a 37.3% reduction in interaction rounds, and a 31.4% improvement in end-to-end latency.<n>We validate WISV on a hardware testbed comprising an NVIDIA Jetson AGX Orin and an A40-equipped server.
  arXiv  Detail & Related papers  (2026-04-20T01:29:56Z)
• Multi-Level Bidirectional Decoder Interaction for Uncertainty-Aware Breast Ultrasound Analysis [2.8218290601001854]
  Breast ultrasound interpretation requires simultaneous lesion segmentation and tissue classification.<n>We propose a multi-task framework addressing these limitations through multi-level decoder interaction and uncertainty-aware adaptive coordination.
  arXiv  Detail & Related papers  (2026-03-01T22:02:06Z)
• SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning [0.0]
  We propose SG-XDEAT, a novel framework for supervised learning on tabular data.<n>At its core, SG-XDEAT employs a dual-stream encoder that decomposes each input feature into two parallel representations.<n>These dual representations are then propagated through a hierarchical stack of attention-based modules.
  arXiv  Detail & Related papers  (2025-10-14T15:56:40Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Suppress and Balance: A Simple Gated Network for Salient Object Detection [89.88222217065858]
  We propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales.
  arXiv  Detail & Related papers  (2020-07-16T02:00:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.