zeanth.github.io/papers.json at main · zeanth/zeanth.github.io · GitHub

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{
  "archive": {
    "name": "ZEanth Laboratory",
    "shortName": "ZL",
    "tagline": "ZEanth Laboratory from Vietnam with love",
    "url": "https://zeanth.github.io",
    "github": "https://github.com/zeanth",
    "logo": "https://avatars.githubusercontent.com/u/290281786?s=400&u=20010d1634539a17c0b9cc1fbcf1339d16052125&v=4",
    "defaultPaperLicense": {
      "spdx": "CC-BY-SA-4.0",
      "name": "Creative Commons Attribution-ShareAlike 4.0 International",
      "url": "https://creativecommons.org/licenses/by-sa/4.0/legalcode.txt"
    }
  },
  "categories": [
    { "code": "cs.CV", "name": "Computer Vision and Pattern Recognition" },
    { "code": "cs.LG", "name": "Machine Learning" },
    { "code": "cs.AI", "name": "Artificial Intelligence" },
    { "code": "cs.CL", "name": "Computation and Language" },
    { "code": "cs.NE", "name": "Neural and Evolutionary Computing" },
    { "code": "cs.CR", "name": "Cryptography and Security" },
    { "code": "cs.SE", "name": "Software Engineering" },
    { "code": "cs.DC", "name": "Distributed, Parallel, and Cluster Computing" },
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    { "code": "cs.HC", "name": "Human-Computer Interaction" },
    { "code": "cs.DB", "name": "Databases" },
    { "code": "cs.IR", "name": "Information Retrieval" }
  ],
  "statuses": [
    { "code": "preprint",      "label": "Preprint",      "order": 1 },
    { "code": "under-review",  "label": "Under Review",  "order": 2 },
    { "code": "accepted",      "label": "Accepted",      "order": 3 },
    { "code": "published",     "label": "Published",     "order": 4 },
    { "code": "withdrawn",     "label": "Withdrawn",     "order": 5 }
  ],
  "papers": [
    {
      "id": "2601.00001",
      "version": "v1",
      "title": "NeuroPath: 3D Multi-Task Deep Learning for MRI Brain Tumor Segmentation and Clinical Interpretation",
      "authors": ["B. N. T. Sang"],
      "categories": ["cs.CV", "cs.LG"],
      "status": "preprint",
      "abstract": "A comprehensive intelligent medical system for automated brain tumor segmentation on MRI, integrating CDFA-Mamba-DolphinNet — a custom 3D hybrid 2D-3D architecture with Mamba State Space Model at the bottleneck. The system features a Biological Rule Engine for anatomical plausibility validation, a Human Trust Score (HTS) for quantitative clinician feedback via PDF annotation, automated RANO 2.0 measurement, anatomical localization, and LLM-based report generation with interactive refinement. Achieves Dice 0.883 and HD95 3.91 mm on BraTS 2023 5-fold cross-validation.",
      "comments": "BraTS 2023 5-fold cross-validation; Dice 0.883, HD95 3.91 mm",
      "journalRef": "Scientific Research Report",
      "doi": null,
      "codeUrl": null,
      "datasetUrl": null,
      "submittedDate": "2026-01-10",
      "history": [
        { "version": "v1", "date": "2026-01-10", "note": "Initial submission" }
      ]
    }
  ]
}