{
 "id": "MedOtter/SegPC-2021",
 "owner": "MedOtter",
 "name": "SegPC-2021",
 "downloads": 637,
 "likes": 0,
 "last_modified": "2026-06-27T02:03:51.000Z",
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 "gated": false,
 "tasks": [
  "image-segmentation"
 ],
 "citation": "1. A. Gupta, R. Duggal, S. Gehlot, R. Gupta, A. Mangal, L. Kumar, N. Thakkar, D. Satpathy,\n   *\"GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic\n   Medical Images,\"* Medical Image Analysis 65, 2020. doi:10.1016/j.media.2020.101788\n2. S. Gehlot, A. Gupta, R. Gupta, *\"EDNFC-Net: Convolutional Neural Network with Nested Feature\n   Concatenation for Nuclei-Instance Segmentation,\"* ICASSP 2020, pp. 1389–1393.\n3. A. Gupta, P. Mallick, O. Sharma, R. Gupta, R. Duggal, *\"PCSeg: Color model driven probabilistic\n   multiphase level set based tool for plasma cell segmentation in multiple myeloma,\"* PLoS ONE\n   13(12): e0207908, 2018. doi:10.1371/journal.pone.0207908\n4. A. Gupta, S. Gehlot, S. Goswami, et al., *\"SegPC-2021: A challenge & dataset on segmentation of\n   Multiple Myeloma plasma cells from microscopic images,\"* Medical Image Analysis 83, 2023.\n   doi:10.1016/j.media.2022.102677\n\nDataset DOI: 10.21227/7np1-2q42 (IEEE DataPort). Source: Kaggle `sbilab/segpc2021dataset`.",
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