Abstract
Surgical phase recognition maps each video frame to a clinically meaningful workflow phase, supporting context-aware assistance, documentation, and postoperative analysis. Most methods treat phase annotations only as class IDs, whereas recent surgical vision-language models often require additional video–text data, captions, or instruction tuning. We propose LaSeD, a label-semantic self-distillation framework that uses phase names as privileged training-time context while retaining
visual-only deployment without a ground-truth phase-name hint. LaSeD initializes a frozen teacher and a student from the same pretrained VLM checkpoint. The teacher receives the frame, a fixed task prompt, and the ground-truth phase-name hint; the student receives the same frame and prompt without the hint, and only its visual encoder is optimized. Training combines hard phase-token supervision with feature-level distillation from cached teacher representations. At inference, the teacher and hint are removed, and the student predicts one of the seven Cholec80 phases through constrained digit-token logits without an additional classifier head. On the Cholec80 evaluation split, LaSeD achieves 86.20% accuracy, 77.75% macro recall, 78.13% macro precision, and 64.15% macro Jaccard. Under the identical protocol, it improves a visual-only Qwen3 VL-4B baseline by 9.45, 9.42, 11.93, and 12.22 percentage points, respectively. Visual-only means that the image is the only sample-specific inference input, while all frames share the same fixed task prompt. These results support the benefit of phase-name-conditioned teacher supervision. Code: GitHub repository.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/COLAS_014.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=XaQW7jBa41
BibTex
@InProceedings{TaoYe_LaSeD_MICCAISAT2026,
author = { Tao, Ye AND Scherl, Claudia AND Homscheid, Sara},
title = { { LaSeD: Label-Semantic Self-Distillation for Visual-Only Surgical Phase Recognition } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS pending},
month = {pending},
page = {pending}
}