all AI news
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples: An Application to Tumour Segmentation for Breast Cancer. (arXiv:2110.10325v2 [cs.LG] UPDATED)
Jan. 13, 2022, 2:10 a.m. | Yongquan Yang
cs.LG updates on arXiv.org arxiv.org
One-step abductive multi-target learning (OSAMTL) is an approach proposed to
handle complex noisy labels. However, OSAMTL is not suitable for the situation
where diverse noisy samples (DNS) are provided for a learning task. In this
paper, giving definition of DNS, we propose one-step abductive multi-target
learning with DNS (OSAMTL-DNS) to expand the original OSAMTL to a wider range
of tasks that handle complex noisy labels. Applying OSAMTL-DNS to tumour
segmentation for breast cancer in medical histopathology whole slide image
analysis, …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Research Assistant/Associate, Health Data Science [LKCMedicine]
@ Nanyang Technological University | NTU Novena Campus, Singapore
Senior Machine Learning Engineer, Portfolio ML
@ Affirm | Remote Canada
[Sessional Lecturer] Foundations of Data Analytics and Machine Learning - APS1070
@ University of Toronto | Toronto, ON, CA
Senior Data Scientist
@ Prosper | United States
Data Analyst
@ ZF Friedrichshafen AG | Coimbatore, TN, IN, 641659