Metraipolis › archive › dc_b4e4e0fcfd60

TME-ContextNet: Class-Conditioned Spatial Supervision and Grouped Decoding for Joint Nuclei Instance Segmentation and Classification — arXiv:2611.02671

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02671
License (as archived): CC0-1.0
Archived: 2026-11-03T18:55:05.749Z
SHA-256 of the archived text: 5e408306a68d7251c09635760d21f0e5e3537d64189dc84f67972bd1cd0926b7
Stable URL of this page: https://metraipolis.com/documento/dc_b4e4e0fcfd60

How to cite

TME-ContextNet: Class-Conditioned Spatial Supervision and Grouped Decoding for Joint Nuclei Instance Segmentation and Classification — arXiv:2611.02671 — arXiv (en), https://arxiv.org/abs/2611.02671 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b4e4e0fcfd60 (Metraipolis archive dc_b4e4e0fcfd60, sha256 5e408306a68d7251c09635760d21f0e5e3537d64189dc84f67972bd1cd0926b7)

BibTeX

@misc{dcb4e4e0fcfd60,
  title = {TME-ContextNet: Class-Conditioned Spatial Supervision and Grouped Decoding for Joint Nuclei Instance Segmentation and Classification — arXiv:2611.02671},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02671},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b4e4e0fcfd60; sha256 5e408306a68d7251c09635760d21f0e5e3537d64189dc84f67972bd1cd0926b7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02671. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Joint nuclei instance segmentation and classification requires precise boundaries and neighborhood information that helps distinguish morphologically similar cell types. We present TME-ContextNet, a multi-task framework that learns class-conditioned spatial context from existing nuclear annotations and organizes decoding into geometry-related and semantic task groups. Class-conditioned spatial supervision extends nuclear labels into a dense target using Euclidean distance transforms, requiring no additional tissue-region annotation. We derive its nearest-region interpretation and characterize its stability to small annotation perturbations. A local--global context module combines dilated convolution and self-attention, while a grouped task decoder reduces redundant computation. The spatial head supplies training supervision and is omitted during inference. Experiments on PanMix, CoNSeP, and PanNuke yield Dice scores of 80.23%, 65.32%, and 79.13%, and mean multi-class panoptic quality (mPQ+) scores of 67.62%, 35.50%, and 43.74%, respectively. PanNuke results are averaged across three test folds. Target and decoder controls support the combination of distance-based supervision and grouped decoding while identifying their dependence on task configuration. The framework uses 36.40 million parameters, compared with 54.74 million for HoVer-Net. These findings support a compact, context-aware formulation for joint nuclei analysis in computational pathology.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.02671 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:16:53.013Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).