arXiv Computer Vision
Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation
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arXiv Computer Vision
Aug 31
DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation
arXiv:2604.12411v2 Announce Type: replace Abstract: Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit ove...
arXiv AI
Sep 1
MedSegBenchmarker: A Raw-Count-First Framework for Controlled 2D Medical Image Segmentation Benchmarks
arXiv:2608.29677v1 Announce Type: cross Abstract: Despite rapid advances in MIS, fair and reproducible comparisons of segmentation models remain challenging due to heterogeneous datasets, inconsisten...
arXiv Machine Learning
Aug 17
TRUE-Colon: Exposing a Consistent Transfer Asymmetry in Real-Time Polyp Detection
arXiv:2608. 13711v1 Announce Type: cross Abstract: Computer-aided detection (CADe) systems for colonoscopy promise to reduce clinical miss rates, yet reliable real-world deployment remains elusive.
arXiv Computer Vision
Sep 25
BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation
The paper introduces BiCC, a bidirectional connected-component loss that pairs annotation- and prediction-derived partitions to score predicted components on their own scale. By deriving instances from predictions, BiCC directly penalizes false-positive components regardless of size, allowing a balance parameter to control the lesion-wise precision–recall trade-off. Across five datasets, BiCC outperforms existing instance-aware losses such as CC-DiceCE and blob loss in lesion-wise F1, and improves over DiceCE on multiple datasets.
arXiv AI
Jul 8
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.
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...