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Tian Y, Bai K, Yu X, Zhu S. Causal multi-label learning for image classification. Neural Netw 2023; 167:626-637. [PMID: 37716214 DOI: 10.1016/j.neunet.2023.08.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/24/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
In this paper, we investigate the problem of causal image classification with multi-label learning. As multi-label learning involves a diversity of supervision signals, it is considered a challenging issue to solve. Previous approaches have attempted to improve performance by identifying label-related image areas or exploiting the co-occurrence of labels. However, these methods are often characterized by complicated procedures, tedious computations, and a lack of intuitive interpretations. To overcome these limitations, we propose a novel approach that incorporates the concept of causal inference, which has been shown to be beneficial in other computer vision problems. Our method, called causal multi-label learning (CMLL), enables the selection of multiple objects from the original image through a multi-class attention module. These objects are then subjected to causal intervention to learn the causal relationships between different labels. Our proposed approach is both elegant and effective, with low computational cost and few parameters required for the multi-class causal intervention approach. Extensive tests and ablation studies demonstrate that the proposed method significantly improves prediction performance without a significant increase in training and inference times.
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Affiliation(s)
- Yingjie Tian
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China; MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, Beijing 100190, China.
| | - Kunlong Bai
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
| | - Xiaotong Yu
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
| | - Siyu Zhu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
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Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M. Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays. Data Augment Label Imperfections (2022) 2022; 13567:112-122. [PMID: 36383493 PMCID: PMC9652771 DOI: 10.1007/978-3-031-17027-0_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This paper aims to identify uncommon cardiothoracic diseases and patterns on chest X-ray images. Training a machine learning model to classify rare diseases with multi-label indications is challenging without sufficient labeled training samples. Our model leverages the information from common diseases and adapts to perform on less common mentions. We propose to use multi-label few-shot learning (FSL) schemes including neighborhood component analysis loss, generating additional samples using distribution calibration and fine-tuning based on multi-label classification loss. We utilize the fact that the widely adopted nearest neighbor-based FSL schemes like ProtoNet are Voronoi diagrams in feature space. In our method, the Voronoi diagrams in the features space generated from multi-label schemes are combined into our geometric DeepVoro Multi-label ensemble. The improved performance in multi-label few-shot classification using the multi-label ensemble is demonstrated in our experiments (The code is publicly available at https://github.com/Saurabh7/Few-shot-learning-multilabel-cxray).
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Affiliation(s)
| | - Saurabh Mahindre
- University at Buffalo, The State University of New York, Buffalo, NY, USA
| | | | | | - Song Wang
- The University of Texas at Austin, Austin, TX, USA
| | - Chunwei Ma
- University at Buffalo, The State University of New York, Buffalo, NY, USA
| | | | - Yifan Peng
- Weill Cornell Medicine, New York, NY, USA
| | - Mingchen Gao
- University at Buffalo, The State University of New York, Buffalo, NY, USA
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