Topology-Aware Query Selection for Surgical Instrument Instance Segmentation

• URL: http://arxiv.org/abs/2608.11607v1
• Date: Wed, 12 Aug 2026 03:29:47 GMT
• Title: Topology-Aware Query Selection for Surgical Instrument Instance Segmentation
• Abstract summary: Final query selection is a relational, variable-cardinality problem rather than a collection of independent candidate decisions.<n>We evaluate topology-aware query selection, which represents the nonempty candidates of a fixed Mask2Former as a complete graph.<n>We show that the evaluated complete path improved coherent instance-set construction from fixed Mask2Former candidates in specified native-instance contracts.
• Score: 8.051653068935332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate foreground masks can still form an incorrect surgical-instrument instance set: duplicate, fragmented, merged, missed, or empty-frame predictions may preserve favorable pixel overlap while violating object identity and count. Final query selection is therefore a relational, variable-cardinality problem rather than a collection of independent candidate decisions. We evaluate topology-aware query selection, which represents the nonempty candidates of a fixed Mask2Former as a complete graph, learns relational candidate and pair representations, predicts set cardinality, and solves an exact structured subset problem. The formal comparison is the complete relational path versus a node-feature-matched path; it evaluates the combined effect of pairwise geometry, message passing, and the additional relational-path capacity, not an isolated component. On the sealed 22-case source test, all three discovery seeds supported instance-set performance improvement with segmentation fidelity and predefined technical-safety preservation: instance F1 increased by 0.0504--0.0612 and positive-frame set-failure rate decreased by 0.0848--0.1060. Direct ROBUST-MIPS transfer reproduced the complete result in all three seeds. Endoscapes supported only one of three seeds and therefore did not establish stable direct transfer. Taken together, the results support a bounded conclusion: the evaluated complete path improved coherent instance-set construction from fixed Mask2Former candidates in specified native-instance contracts, while stable cross-domain transfer and component-specific effects remain unestablished.

Related papers

• SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation [51.46828526392219]
  Reasoning segmentation converts an implicit linguistic conclusion into a precise mask.<n>Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context.<n>We present SeGDeP, an explicit what-where interface.
  arXiv  Detail & Related papers  (2026-09-08T15:11:32Z)
• SAM3-LoRA: Parameter-Efficient Adaptation of a Concept-Promptable Foundation Model for Multi-Class Structural Defect Segmentation [0.0]
  This study applies Low-Rank Adaptation (LoRA) to SAM3 for multi-class structural defect segmentation.<n>We describe a supervision procedure that trains a concept-promptable model directly from COCO-style class-labeled instance segmentation.
  arXiv  Detail & Related papers  (2026-08-31T23:19:59Z)
• A Pre-Specified Construction-Confirmation Test of Operation-Level Causal Transfer Across Finite Isomorphic Symbolic Domains [0.0]
  We ask if the hidden-state difference between two operations is estimated separately for each source input, does adding that difference move the model toward the corresponding recipient answer?<n>The design compares this input-specific intervention with wrong-operation, norm-matched random, and no-op controls, and separates candidate construction from an independently isolated confirmation split.
  arXiv  Detail & Related papers  (2026-08-16T15:37:26Z)
• SEER: A Self-Grounded Evidence Interface for Controlled Spatial Relation Classification [71.9783246097687]
  SEER hides candidate relations during pair localization, constructs a query-specific view with explicit subject/object roles, and retains the full image and sparse box geometry as complementary evidence.<n>For relation-choice protocols with exact inverse support, an optional refinement swaps the entity roles and changes the forward decision only when exactly one visual state obeys the corresponding inverse relation.<n>The unchanged protocol yields +4.35 to +11.79 on all 2,434 filtered EmbSpatial pair-relation questions across three models.
  arXiv  Detail & Related papers  (2026-08-04T13:16:15Z)
• The Resolution of Causal Heterogeneity [5.222979968110479]
  Causal subgroup analyses often report a small number of groups summarizing treatment effect heterogeneity, as if that number were a well-defined estimand.<n>We replace it with a new population estimand, the resolution profile, a functional of the causal feature law.<n>Inference is organized around one cross-fitted Bayesian-bootstrap posterior for a single structured moment process.
  arXiv  Detail & Related papers  (2026-07-19T14:56:06Z)
• Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation [0.0]
  ACFA replicates a content-addressed OR-Set of signed contributions and a grow-only set of self-authenticating equivocation proofs.<n>A prototype (10 nodes, 3 Byzantine) passes 16/16 falsification checks.
  arXiv  Detail & Related papers  (2026-07-11T13:23:06Z)
• Identifiability of Relational Queries in Multi-View Pretraining [0.500301731167245]
  Two globally consistent worlds can agree on every shared attribute yet disagree on the query answer.<n>This ambiguity is structural -- a property of the interface design, not the data volume.<n>We formalize query identifiability for data integration under interface laws.
  arXiv  Detail & Related papers  (2026-07-06T07:18:00Z)
• Bayesian Wind Tunnels for Model Selection [0.4779196219827507]
  We show that transformers can perform exact Bayesian filtering within a fixed hypothesis class.<n>We then identify a sharp perceptual access condition: when the discnative statistic requires arithmetic, model selection succeeds with integer tokens.<n>A stationarity control confirms the operative factor: stable semantics, not integer identity, enable circuit compilation.
  arXiv  Detail & Related papers  (2026-07-01T04:05:08Z)
• RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning [2.0052993723676895]
  RobustModelMaker is a Python framework that bootstraps stability selection with strict nested cross-validation.<n>It produces a stability-tested feature subset together with a leakage-safe performance estimate.<n>It supports nine algorithms across binary classification, multiclass classification, and regression.
  arXiv  Detail & Related papers  (2026-06-01T02:11:23Z)
• SEED: Targeted Data Selection by Weighted Independent Set [76.68391670109433]
  We develop a robust and scalable data selection pipeline dubbed SEED.<n>SEED consistently outperforms state-of-the-art methods on instruction tuning, visual instruction tuning, and semantic segmentation.
  arXiv  Detail & Related papers  (2026-05-15T07:26:54Z)
• SliceGraph: Mapping Process Isomers in Multi-Run Chain-of-Thought Reasoning [60.650808962786364]
  We propose SliceGraph, a problem-model-cell graph built by mutual-kNN over sparse activation-key Jaccard similarity between CoT slices.<n>We show that correct CoTs sharing the same normalized answer split into multiple process families.<n>We also show that process families navigate the same atlas with distinct transition kernels under matched null controls.
  arXiv  Detail & Related papers  (2026-05-14T09:37:26Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• Semi-DETR: Semi-Supervised Object Detection with Detection Transformers [105.45018934087076]
  We analyze the DETR-based framework on semi-supervised object detection (SSOD) We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector. Our method outperforms all state-of-the-art methods by clear margins.
  arXiv  Detail & Related papers  (2023-07-16T16:32:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.