BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring

Istiak Ahmed; Kazi Shahriar Sanjid; Galib Ahmed; Md. Tanzim Hossain; Md. Anwarul Islam; Shahrukh Khan; Md. Ashrif Rahman Arian; Md. Nishan Khan; Md. Misbah Khan; S M Hasibul Hoque; Rahnuma Shahrin Rista; Md. Jobairul Islam; Sheikh Anisul Haque; Md Arifur Rahman; Syed Md. Akram Hussain; Syeda Nashra; Sayeed Shafayet Chowdhury; Md. Mostafa Kamal Sarker; M. Monir Uddin

arXiv · 2607.11949

BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring

Istiak Ahmed·Kazi Shahriar Sanjid·Galib Ahmed·Md. Tanzim Hossain·Md. Anwarul Islam·Shahrukh Khan·Md. Ashrif Rahman Arian·Md. Nishan Khan·Md. Misbah Khan·S M Hasibul Hoque·Rahnuma Shahrin Rista·Md. Jobairul Islam·Sheikh Anisul Haque·Md Arifur Rahman·Syed Md. Akram Hussain·Syeda Nashra·Sayeed Shafayet Chowdhury·Md. Mostafa Kamal Sarker·M. Monir Uddin

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Abstract

We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transformer with Region-Aware Mamba (BAT-RM), a hybrid architecture that integrates Sobel-gated boundary attention, a linear-time, multi-directional Mamba module for long-range context, and a boundary-skeleton-guided fusion gate. This design achieves linear-time complexity for long-range context modeling, avoiding the quadratic cost of full spatial self-attention. The full pipeline spans multi-institutional data collection, rigorous inter-rater quality assurance, external validation in an independent cohort, and a web-based clinical interface natively compatible with Varian, RayStation, and Monaco. Against four baselines, BAT-RM achieves superior performance across seven anatomical classes, with statistically significant improvements in target volumes, including GTV and CTV, and in organs at risk such as the rectum and bladder. A prospective multi-center reader study involving 13 radiation oncologists demonstrated that AI assistance elevates junior oncologists' IoU from 0.899 to 0.965, approaching senior-level accuracy, while reducing contouring time by more than 80%. The system also reduced expert consultation rates and improved inter-reader consistency, reflecting gains in both efficiency and quality assurance. Following clinical deployment at a partner hospital, the system reduced patient wait times from days to hours without additional staffing, enabling same-day or next-day initiation of treatment for routine cases. BAT-RM demonstrates that a rigorous research pipeline, from data curation to clinical deployment, can translate directly into measurable patient benefit in resource-constrained settings where the demand for radiotherapy far exceeds specialist capacity.

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Istiak Ahmed, Kazi Shahriar Sanjid, Galib Ahmed, Md. Tanzim Hossain, Md. Anwarul Islam, Shahrukh Khan, Md. Ashrif Rahman Arian, Md. Nishan Khan, Md. Misbah Khan, S M Hasibul Hoque, Rahnuma Shahrin Rista, Md. Jobairul Islam, Sheikh Anisul Haque, Md Arifur Rahman, Syed Md. Akram Hussain, Syeda Nashra, Sayeed Shafayet Chowdhury, Md. Mostafa Kamal Sarker, M. Monir Uddin. 2026-07-11. BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring. https://arxiv.org/abs/2607.11949

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