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Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports [PDF]

Entropy, 2022
The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (single-
Kokten Ulas Birant, Derya Birant
doaj   +2 more sources

Learning and Interpreting Multi-Multi-Instance Learning Networks [PDF]

, 2018
JMLR
Alessandro Tibo, Manfred Jaeger, Paolo Frasconi   +2 more
core   +8 more sources

Enhancing unsupervised medical entity linking with multi-instance learning [PDF]

BMC Medical Informatics and Decision Making, 2021
Background A lot of medical mentions can be extracted from a huge amount of medical texts. In order to make use of these medical mentions, a prerequisite step is to link those medical mentions to a medical domain knowledge base (KB).
Cheng Yan, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yafei Shi, Shengping Liu   +5 more
doaj   +2 more sources

CT-Based 2.5D Deep Learning-Multi-Instance Learning for Predicting Early Recurrence of Hepatocellular Carcinoma and Correlating with Recurrence-Related Pathological Indicators [PDF]

Journal of Hepatocellular Carcinoma
Yongyi Cen,1,2,* Haiyang Nong,1,2,* Dehui Du,3,* Yingning Wu,1,2 Jianpeng Chen,1,2 Zhaolin Pan,4 Yin Huang,5 Ke Ding,6 Deyou Huang1,2 1Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities ...
Cen Y, Nong H, Du D, Wu Y, Chen J, Pan Z, Huang Y, Ding K, Huang D   +8 more
doaj   +2 more sources

Geometric multi-instance learning for weakly supervised gastric cancer segmentation [PDF]

npj Digital Medicine
Weakly supervised segmentation of cancerous regions in whole-slide images (WSIs) is a crucial task in computational pathology, but it is severely hampered by the need for expensive pixel-level annotations.
Chenshen Huang, Haoyun Xia, Xi Xiao, Hong Chen, Yiqing Jiang, Yahui Lyu, Zhizhan Ni, Tianyang Wang, Ning Wang, Qi Huang   +9 more
doaj   +2 more sources

Thyroid pathology image classification via multi-scale feature fusion and multi-instance learning [PDF]

Diagnostic Pathology
Background The global incidence of thyroid cancer has significantly increased, while traditional pathological diagnosis remains time-consuming and expert-dependent.
Xiangzhi Li, Guanxin Liu, Mengmeng Sun, Lu Wang, Bingdou He, Kefen Zhang, Shimei Zhao, Kaisheng Xie, Yuwei Jiang, Yajun Ying, Ning Liao, Xiaobo Yang   +11 more
doaj   +2 more sources

Weakly supervised large-scale pancreatic cancer detection using multi-instance learning [PDF]

Frontiers in Oncology
IntroductionEarly detection of pancreatic cancer continues to be a challenge due to the difficulty in accurately identifying specific signs or symptoms that might correlate with the onset of pancreatic cancer.
Shyamapada Mandal, Keerthiveena Balraj, Hariprasad Kodamana, Hariprasad Kodamana, Chetan Arora, Chetan Arora, Julie M. Clark, David S. Kwon, David S. Kwon, Anurag S. Rathore, Anurag S. Rathore   +10 more
doaj   +2 more sources

Protein-ligand binding affinity prediction using multi-instance learning with docking structures [PDF]

Frontiers in Pharmacology
IntroductionRecent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery.
Hyojin Kim, Heesung Shim, Aditya Ranganath, Stewart He, Garrett Stevenson, Jonathan E. Allen   +5 more
doaj   +2 more sources

Cross-scale multi-instance learning for pathological image diagnosis. [PDF]

Med Image Anal
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches).
Deng R, Cui C, Remedios LW, Bao S, Womick RM, Chiron S, Li J, Roland JT, Lau KS, Liu Q, Wilson KT, Wang Y, Coburn LA, Landman BA, Huo Y.   +14 more
europepmc   +4 more sources

Multiview Multi-Instance Multilabel Active Learning [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2022
Multiview multi-instance multilabel learning (M3L) is a framework for modeling complex objects. In this framework, each object (or bag) contains one or more instances, is represented with different feature views, and simultaneously annotated with a set of nonexclusive semantic labels.
Guoxian Yu, Yuying Xing, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang   +4 more
openaire   +3 more sources