Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Nam, Siwoo | - |
dc.contributor.author | Kang, Myeongkyun | - |
dc.contributor.author | Won, Dongkyu | - |
dc.contributor.author | Chikontwe, Philip | - |
dc.contributor.author | Noh, Byeong-Joo | - |
dc.contributor.author | Go, Heounjeong | - |
dc.contributor.author | Park, Sang Hyun | - |
dc.date.accessioned | 2023-12-26T18:12:50Z | - |
dc.date.available | 2023-12-26T18:12:50Z | - |
dc.date.created | 2022-12-30 | - |
dc.date.issued | 2022-09-22 | - |
dc.identifier.isbn | 9783031169182 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/46814 | - |
dc.description.abstract | In Whole Slide Image (WSI) analysis, detecting nuclei subtypes such as Tumor Infiltrating Lymphocytes (TILs) which are a primary bio-marker for cancer diagnosis, is an important yet challenging task. Though several conventional methods have been proposed and applied to target user's nuclei sub-types (e.g., TILs), they often fail to detect subtle differences between instances due to similar morphology across sub-types. To address this, we propose a novel decoupled segmentation architecture that leverages point annotations in a weakly-supervised manner to adapt to the nuclei sub-type. Our design consists of an encoder for feature extraction, a boundary regressor that learns prior knowledge from nuclei boundary masks, and a point detector that predicts the center positions of nuclei, respectively. Moreover, employing a frozen pre-trained nuclei segmenter facilitates easier adaptation to TILs segmentation via fine-tuning, while learning a decoupled point detector. To demonstrate the effectiveness of our approach, we evaluated on an in-house Melanoma TIL dataset, and report significant improvements over a state-of-the-art weakly-supervised TILs segmentation method, including conventional approaches based on pseudo-label construction. | - |
dc.language | English | - |
dc.publisher | PRIME-MICCAI 2022 Organizers | - |
dc.title | Weakly-Supervised TILs Segmentation Based on Point Annotations Using Transfer Learning with Point Detector and Projected-Boundary Regressor | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1007/978-3-031-16919-9_11 | - |
dc.identifier.scopusid | 2-s2.0-85140430404 | - |
dc.identifier.bibliographicCitation | 5th International Workshop on Predictive Intelligence in Medicine (PRIME MICCAI), pp.115 - 125 | - |
dc.identifier.url | https://basira-lab.com/prime-miccai-2022/ | - |
dc.citation.conferencePlace | SI | - |
dc.citation.conferencePlace | Singapore | - |
dc.citation.endPage | 125 | - |
dc.citation.startPage | 115 | - |
dc.citation.title | 5th International Workshop on Predictive Intelligence in Medicine (PRIME MICCAI) | - |
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