A survey of human-in-the-loop for machine learning
Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …
including computer vision, natural language processing, speech processing tasks, etc …
Generative adversarial networks (gans) for medical image processing: Recent advancements
Abstract Generative Adversarial Networks (GANs) constitute an advanced category of deep
learning models that have significantly transformed the domain of generative modelling …
learning models that have significantly transformed the domain of generative modelling …
A human-in-the-loop method for pulmonary nodule detection in CT scans
Automated pulmonary nodule detection using computed tomography scans is vital in the
early diagnosis of lung cancer. Although extensive well-performed methods have been …
early diagnosis of lung cancer. Although extensive well-performed methods have been …
Do humans and machines have the same eyes? human-machine perceptual differences on image classification
Trained computer vision models are assumed to solve vision tasks by imitating human
behavior learned from training labels. Most efforts in recent vision research focus on …
behavior learned from training labels. Most efforts in recent vision research focus on …
Unsupervised network learning for cell segmentation
Cell segmentation is a fundamental and critical step in numerous biomedical image studies.
For the fully-supervised cell segmentation algorithms, although highly effective, a large …
For the fully-supervised cell segmentation algorithms, although highly effective, a large …
Active learning in brain tumor segmentation with uncertainty sampling, annotation redundancy restriction, and data initialization
DD Kim, RS Chandra, J Peng, J Wu, X Feng… - arXiv preprint arXiv …, 2023 - arxiv.org
Deep learning models have demonstrated great potential in medical 3D imaging, but their
development is limited by the expensive, large volume of annotated data required. Active …
development is limited by the expensive, large volume of annotated data required. Active …
Suggestive annotation of brain MR images with gradient-guided sampling
Abstract Machine learning has been widely adopted for medical image analysis in recent
years given its promising performance in image segmentation and classification tasks. The …
years given its promising performance in image segmentation and classification tasks. The …
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification
Federated Learning (FL) enables multiple institutes to train models collaboratively without
sharing private data. Current FL research focuses on communication efficiency, privacy …
sharing private data. Current FL research focuses on communication efficiency, privacy …
Deep reinforced active learning for multi-class image classification
E Slade, KM Branson - arXiv preprint arXiv:2206.13391, 2022 - arxiv.org
High accuracy medical image classification can be limited by the costs of acquiring more
data as well as the time and expertise needed to label existing images. In this paper, we …
data as well as the time and expertise needed to label existing images. In this paper, we …
[PDF][PDF] 人在回路型AI 训练的基本流程与交互模型研究
余欣， 朝乐门， 孟刚 - 情报资料工作, 2022 - qbzl.ruc.edu.cn
[目的/意义] 如何使AI (Artificial Intelligence) 智能体在内的AI 生态系统能够“明物理,
通事理和懂人理” 是AI 治理的核心命题. 而AI 训练, 尤其是人在回路型AI 训练为此问题提供了 …
通事理和懂人理” 是AI 治理的核心命题. 而AI 训练, 尤其是人在回路型AI 训练为此问题提供了 …