Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches
COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
Data banzhaf: A robust data valuation framework for machine learning
Data valuation has wide use cases in machine learning, including improving data quality
and creating economic incentives for data sharing. This paper studies the robustness of data …
and creating economic incentives for data sharing. This paper studies the robustness of data …
[HTML][HTML] Data-driven learning for data rights, data pricing, and privacy computing
In recent years, data has become one of the most important resources in the digital
economy. Unlike traditional resources, the digital nature of data makes it difficult to value …
economy. Unlike traditional resources, the digital nature of data makes it difficult to value …
When does contrastive visual representation learning work?
Recent self-supervised representation learning techniques have largely closed the gap
between supervised and unsupervised learning on ImageNet classification. While the …
between supervised and unsupervised learning on ImageNet classification. While the …
Fair federated medical image segmentation via client contribution estimation
How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …
investigated how to reward clients based on their contribution (collaboration fairness), and …
Consortium blockchain-enabled smart ESG reporting platform with token-based incentives for corporate crowdsensing
Environmental, social and governance (ESG) issues arouse wide concern in both industry
and academia to promote sustainable development. Listed companies assume the …
and academia to promote sustainable development. Listed companies assume the …
Opendataval: a unified benchmark for data valuation
Assessing the quality and impact of individual data points is critical for improving model
performance and mitigating undesirable biases within the training dataset. Several data …
performance and mitigating undesirable biases within the training dataset. Several data …
Data-oob: Out-of-bag estimate as a simple and efficient data value
Data valuation is a powerful framework for providing statistical insights into which data are
beneficial or detrimental to model training. Many Shapley-based data valuation methods …
beneficial or detrimental to model training. Many Shapley-based data valuation methods …
Additive mil: Intrinsically interpretable multiple instance learning for pathology
Abstract Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading, predicting …
solving critical problems such as automating cancer diagnosis and grading, predicting …
Data analysis with Shapley values for automatic subject selection in Alzheimer's disease data sets using interpretable machine learning
L Bloch, CM Friedrich… - Alzheimer's Research & …, 2021 - Springer
Background For the recruitment and monitoring of subjects for therapy studies, it is important
to predict whether mild cognitive impaired (MCI) subjects will prospectively develop …
to predict whether mild cognitive impaired (MCI) subjects will prospectively develop …