Zobrazeno 1 - 10
of 17 454
pro vyhledávání: '"Multiple-Instance Learning"'
Expression of human epidermal growth factor receptor 2 (HER2) is an important biomarker in breast cancer patients who can benefit from cost-effective automatic Hematoxylin and Eosin (H\&E) HER2 scoring. However, developing such scoring models require
Externí odkaz:
http://arxiv.org/abs/2411.05028
Though multiple instance learning (MIL) has been a foundational strategy in computational pathology for processing whole slide images (WSIs), current approaches are designed for traditional hematoxylin and eosin (H&E) slides rather than emerging mult
Externí odkaz:
http://arxiv.org/abs/2411.08975
Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded. However, previous UC classification methods (i.e., image-level estimation) mainly assumed the in
Externí odkaz:
http://arxiv.org/abs/2411.14750
Whole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention is focused on Multiple Instance Learning (MIL) using static datasets.
Externí odkaz:
http://arxiv.org/abs/2410.10573
Autor:
Saada, Thiziri Nait, Di Proietto, Valentina, Schmauch, Benoit, Von Loga, Katharina, Fidon, Lucas
Multiple Instance Learning (MIL) models have proven effective for cancer prognosis from Whole Slide Images. However, the original MIL formulation incorrectly assumes the patches of the same image to be independent, leading to a loss of spatial contex
Externí odkaz:
http://arxiv.org/abs/2408.00427
Autor:
Castro-Macías, Francisco M., Morales-Álvarez, Pablo, Wu, Yunan, Molina, Rafael, Katsaggelos, Aggelos K.
Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically seeks predictions at both bag and instance levels (classification and loc
Externí odkaz:
http://arxiv.org/abs/2410.03276
Computational pathology (CPath) has significantly advanced the clinical practice of pathology. Despite the progress made, Multiple Instance Learning (MIL), a promising paradigm within CPath, continues to face challenges, particularly related to incom
Externí odkaz:
http://arxiv.org/abs/2408.15032
Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analytical challenges. Multiple Instance Learning (MIL) has emerged as a pow
Externí odkaz:
http://arxiv.org/abs/2408.09476
Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example,
Externí odkaz:
http://arxiv.org/abs/2408.04813