BTI-Net: Bidirectional Decoder-Level Task Interaction via Uncertainty-Aware Gating for Multi-Task Medical Image Analysis
arXiv:2606.29102v1 Announce Type: cross 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 multiplica
The continuous advancements in AI and deep learning research allow for increasingly sophisticated architectural innovations in specialized domains like medical imaging.
Improved multi-task learning for medical image analysis can lead to more accurate, efficient, and potentially automated diagnostic tools, reducing human error and accelerating patient care.
The explicit bidirectional communication between decoder levels in multi-task models for medical imaging moves beyond traditional independent or encoder-shared task learning, enabling more synergistic information exchange.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Medical imaging companies
- · Traditional single-task medical image analysis methods
More robust and accurate AI models for combined medical image segmentation and classification tasks will emerge.
Accelerated development of AI-driven diagnostic platforms leading to earlier disease detection and personalized treatment plans.
Reduced burden on radiologists and pathologists, allowing them to focus on more complex cases while improving global access to high-quality diagnostics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG