{
  "abstract": "Accurate medical image segmentation and the diagnostic decisions it supports are increasingly constrained by the cost of supervision: dense voxel-level annotation by expert clinicians remains slow, expensive, and difficult to scale, leaving many anatomies, pathologies, and modalities sparsely labeled. A large literature therefore relaxes the demand for full masks, yet existing surveys often catalog broad forms of limited supervision without directly comparing the practical trade-off between label granularity, annotation burden, and segmentation quality. This review focuses on weakly and coarsely labeled settings (image-level tags, points/clicks, bounding boxes, scribbles, and coarse masks, together with gaze or partial annotations as separate, protocol-dependent forms of supervision) and organizes them through an annotation-economics perspective that considers per-case annotation cost, required expertise, scalability, and the reported performance gap relative to full supervision. The evidence suggests that box and scribble supervision can provide favorable cost–performance trade-offs in several medical segmentation studies, although the reported metrics and datasets are heterogeneous and should not be interpreted as a unified quantitative frontier. We next examine the foundation-model turn, namely the Segment Anything Model and its medical derivatives, and argue that promptable models re-encode rather than eliminate human supervision, moving part of the effort from offline labeling to inference-time interaction. Finally, we extend the analysis beyond Dice to ask whether weakly produced masks support downstream clinical tasks: aggregate measurements may be relatively tolerant of moderate boundary error, whereas texture and high-order radiomic features can be substantially more sensitive. We close by identifying benchmark fragmentation, annotation variability, and the still-limited evidence connecting weak segmentation to clinical outcomes as open problems and propose a unified “weak label → task” benchmark for future evaluation.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China",
    "School of Science, Harbin Institute of Technology, Shenzhen 518055, China",
    "Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiu Shu",
      "orcid": "0009-0008-2461-4126"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinming Zhang",
      "orcid": "0000-0003-3997-6425"
    },
    {
      "affs": [
        2
      ],
      "name": "Di Yuan",
      "orcid": "0000-0001-9403-1112"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1vwtk95x2pavk.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213862",
  "fulltext_url": null,
  "id": "1vwtk95x2pavk",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3862",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7223212562",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/1vwtk95x2pavk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14213862",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3862"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "From Weakly Supervised Segmentation to Diagnostic Decisions: An Annotation-Economics Review for Medical Imaging",
  "title_text": "From Weakly Supervised Segmentation to Diagnostic Decisions: An Annotation-Economics Review for Medical Imaging",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/1vwtk95x2pavk",
  "venue": "Mathematics",
  "versions": []
}