arXiv Computer Vision
Rethinking Data Imbalance in Class Incremental Surgical Instrument Segmentation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Machine Learning
Aug 5
MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery
arXiv:2605. 20297v2 Announce Type: replace-cross Abstract: Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning.
arXiv Computer Vision
Aug 26
Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation
arXiv:2608.24541v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding...
arXiv Computer Vision
Sep 23
Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction
arXiv:2609.25850v1 Announce Type: new Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-s...
Hugging Face Trending Papers
Aug 25
Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation
Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding and clinical assistance. Recently, adapting foun...
arXiv Machine Learning
Aug 3
Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation
arXiv:2607. 29509v1 Announce Type: cross Abstract: Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relatively lower proportions of small and limitedly exposed structures.
arXiv Computer Vision
Sep 16
TEDi: Temporal Memory-Enhanced and Denoising Transformer for Surgical Instrument Segmentation
TEDi is a Temporal memory-Enhanced and Denoising Transformer designed for surgical instrument segmentation. It introduces a query-level memory bank with a memory search enhancement encoder to incorporate discriminative representations from past frames, and a temporal consistency denoising module that builds a cross‑frame semantic anchor to stabilize predictions. Experiments on EndoVis 2017 and EndoVis 2018 show that TEDi outperforms existing state‑of‑the‑art methods, indicating its effectiveness for computer‑assisted surgery.