Pathologist REasoning-Guided REport Generation Challenge¶

Important dates¶

🕐 Timezone: Times are given in Korea Standard Time (KST) and Coordinated Universal Time (UTC) for the same instant. KST = UTC+9 year-round.

📎 Additional deadlines (workshop, proceedings, camera-ready) — TBD on the official challenge pages.

The Top-3 ranked teams will be officially announced during the MICCAI 2026 conference event (27 September – 1 October 2026). Certificates of Merit will be awarded to the top five performing teams. Additional monetary awards — pending confirmation from societies, industrial partners, and MICCAI SIGs.

Notice¶

Official notices (policy, data access, deadlines, or platform changes). Newest first.

📌 15 April 2026 — Save the dates¶

REG² will follow the timeline in Important dates above. Workshop, proceedings, and camera-ready deadlines remain TBD and will be posted when confirmed.

Updates¶

Short news and release announcements. Newest first (latest at the top).

📢 15 April 2026 — Welcome to REG²¶

Pathologist Reasoning-Guided Report Generation Challenge (REG²) builds on REG2025 by benchmarking diagnostic reasoning alongside report generation. Follow Notice and Updates for releases and rule changes.

Challenge overview¶

REG² is a benchmark for generating pathology reports from gigapixel-scale whole slide images (WSIs) while making the diagnostic reasoning process explicit. The challenge builds on REG2025, which measured report generation quality but relied on metrics that only partially reflect clinical needs. REG² asks participants to produce not only reports but structured reasoning aligned with pathologist exploration and reportability decisions. An agenda that connects naturally to agentic AI (planning, tool use, and multi-step decision-making) while addressing the scarcity of rigorous medical reasoning evaluation data.

Motivation¶

Pathology examination is often the final confirmation and gold standard in diagnosis, and the written report is central to downstream clinical decisions. Producing that report is difficult: pathologists must synthesize enormous WSIs and attend to fine-grained, clinically decisive features. Recent vision–language models have shown strong results on automated report generation despite gigapixel complexity. REG2025 established a useful baseline where finalists achieved encouraging performance under that setting.

However, REG2025 evaluation leaned on surface-level lexical scores (e.g., ROUGE-L, BLEU-4) and shallow semantic tools (e.g., SciSpacy, OpenBioLLM). Those metrics do not capture what matters clinically: clinical correctness, negation and omission of safety-critical findings. REG² is designed to close that gap by making diagnostic reasoning central to data and evaluation. It focuses on how pathologists explore evidence, compare diagnoses, and decide what belongs in the report.

From REG2025 to REG²¶

REG2025 focused on how well models matched reference reports. REG² extends that goal by integrating a reasoning trajectory that precedes and supports the report: the same kind of structured thinking pathologists use to justify conclusions and explain them to colleagues. Modern agentic systems are well suited to decompose such workflows, but the community still lacks standardized, clinically grounded benchmarks for reasoning on WSIs. REG² aims to supply that reasoning benchmark and compare agentic vs non-agentic approaches on a common footing.