SELF-LLP: Self-supervised learning from label proportions with self-ensemble

Article Computer Science, Artificial Intelligence

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images

Qinghao Ye et al.

Summary: The ongoing global pandemic of COVID-19 highlights the importance of CT scans in assessing infection severity and identifying carriers. The multi-domain shift problem due to different appearances in CT images necessitates a reliable and efficient recognition model to address this issue effectively and accurately.

APPLIED SOFT COMPUTING (2022)

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Article Computer Science, Artificial Intelligence

Discrepant multiple instance learning for weakly supervised object detection

Wei Gao et al.

Summary: D-MIL introduces discrepantly collaborative modules into MIL, creating complementary solutions for precise object localization through multiple MIL learners. The teachers-students model improves performance by providing rich information and absorbing complementary knowledge from multiple teachers. D-MIL achieves state-of-the-art performance on the challenging MS-COCO object detection benchmark.

PATTERN RECOGNITION (2022)

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Article Computer Science, Artificial Intelligence

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Xiaoming Liu et al.

Summary: This paper proposed a novel weakly supervised segmentation method for COVID-19 infections in CT slices, which only requires scribble supervision and is enhanced with the uncertainty-aware self-ensembling and transformation consistent techniques. Experimental results demonstrate that the proposed method is more effective than other weakly supervised methods and achieves similar performance as those fully supervised.

PATTERN RECOGNITION (2022)

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Article Computer Science, Artificial Intelligence

CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection

Jiabin Zhang et al.

Summary: The proposed weakly supervised learning method, CADN, is designed for surface defect detection. It can be trained with image tag annotations only and performs image classification and defect localization simultaneously, with good real-time performance and high accuracy achieved.

PATTERN RECOGNITION (2021)

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Article Computer Science, Artificial Intelligence