arXiv:2607. 13237v1 Announce Type: cross Abstract: Precise spatial-temporal annotation of laparoscopic videos is time-consuming and requires expert knowledge.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks such as phase recognition, step recognition and anticipation benefit from dense frame-level supervision, whereas pixel-level spatial tasks including instrument segmentation and action recognition are only sparsely annotated on selected keyframes due to prohibitive labeling costs. This supervision imbalance undermines shared representation learning and limits joint optimization across heterogeneous surgical tasks.
The paper presents a method for localizing functional surgical landmarks—specifically instrument tips and anchors—in surgical videos without requiring manual pixel-level mask annotations. It leverages vision foundation models, such as SAM 3, to generate dense structural priors through zero‑shot, point‑prompted masks, and refines landmark predictions with a lightweight, coarse‑to‑fine multi‑frame network. Experiments on 7,867 clips from 60 videos show that the approach achieves F1 scores of 72.4% for tip and 58.0% for anchor localization, with ablations confirming the benefits of structural priors and refinement stages.
arXiv:2607.12896v3 Announce Type: replace Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
The paper introduces ExiL, a mask‑conditioned progressive learning framework for bone ultrasound segmentation that models annotation as a structured refinement trajectory. ExiL uses a synthetic expert‑like brush simulator and a lightweight U‑Net to learn from imperfect masks, and it can be updated in real time from expert refinements. In experiments on UltraBones100k and a prospective volunteer dataset, ExiL cut average annotation time from 60 to 20 seconds per frame and improved mean Dice by about 0.045, achieving 0.87 Dice and 2.7 px boundary error with 10–50 ms inference.
The paper proposes a two‑stage learning framework for multi‑organ segmentation that handles partially annotated datasets and domain shifts. First, the model learns accurate segmentations from available annotations to build robust feature representations. Second, it introduces learnable organ prototypes and a Sinkhorn‑triplet loss to enforce organ‑wise feature consistency across datasets, keeping embeddings of the same organ close while separating different organs, even when annotations are missing.
SurgMotion is a video-native foundation model that replaces pixel-level reconstruction with latent motion prediction for surgical video analysis. It introduces motion-guided masked prediction, spatiotemporal affinity self-distillation, and spatiotemporal feature diversity regularization to focus on semantically meaningful regions and avoid representation collapse. Trained on SurgMotion-15M, the largest surgical video dataset, it outperforms state-of-the-art methods across 17 benchmarks, improving workflow recognition, action triplet recognition, skill assessment, polyp segmentation, and depth estimation.