Abstract
We describe our submission to the MedReason 2026 challenge, covering multiple-choice (MCQ) and open-ended (OE) medical visual question answering (VQA) under fully offline, containerized inference. Our first finding is that MCQ retrieval must compare answer semantics rather than answer labels: labels are independently assigned per question, so copying a retrieved neighbor’s label transfers no useful information, whereas scoring each current option’s text against correct-answer text from similar training cases raises retrieval-only accuracy from 20.0% to 57.5% on a 200-case retrieval-excluded development holdout. Our second finding attributes the submitted system’s accuracy: holding the task-specific MCQ Low-Rank Adaptation (LoRA) adapter fixed and varying the number k of in-prompt retrieved examples changes accuracy by at most one case — 187/200 (93.5%) at both k = 0 and the adapter’s training-time k = 1, 188/200 (94.0%) at the packaged runtime’s default k = 3 — and the submitted confidence-gated override adds no net accuracy on top of k = 3, selecting the VLM in 198/200 cases. With the final MCQ adapter fixed, retrieval changes accuracy by at most one case, and gating provides no net gain. On 20 OE cases, token-F1 and RaTEScore [14] decrease as k grows, but paired sign tests on token-F1 differences are nonsignificant (p ≥ 0.29); a single-annotator comparison found 6/20 wrong-anchor errors for the final configuration and 14/20 for an earlier configuration that jointly differed in routing, adapter, and prompting. The system reaches 94.0% MCQ accuracy on the development holdout and 93.20% on the organizer’s official pre-evaluation, versus 29.43% for the off-the-shelf reference baseline, while both of the organizer’s open-ended scores are lower than that baseline’s (ground-truth agreement 1.245 versus 1.588, visual accuracy 1.995 versus 2.696, each out of 4). Code and configuration are available at https://github.com/Kirscher/MedReason2026.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MedReason_005.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=0vvODUp46Q
BibTex
@InProceedings{KirTri_OptionAware_MICCAISAT2026,
author = { Kirscher, Tristan AND Koser, Niklas C. AND Pirk, Soren},
title = { { Option-Aware Retrieval and Task-Specific VLM Adaptation for Medical VQA } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17262},
month = {pending},
page = {pending}
}