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Deep Gaussian mixture model based instance relevance estimation for multiple instance learning applications

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Abstract

Multiple instance learning (MIL) is a type of supervised learning, where instead of receiving a collection of individually labeled examples, the learner is given weakly labeled bags of instances. If the bag contains at least one positive instance, the bag is assigned a positive label, otherwise, the bag is assigned a negative label. The positive bags in MIL may contain instances from different classes, which results in instance-level ambiguity in the bag and complicates the learning process. In this case, identifying relevant instances is important in bag classification and model interpretation. To identify the relevant instances in MIL, this paper proposes a deep subspace-based Gaussian mixture model instance relevance estimation network with Fisher vector encoding (DGMIR-FV). To be specific, the proposed approach uses an estimation network for instance relevance estimation and selects the instances from each bag based on relevance scores. Afterwards, selected instances are encoded using the Fisher vector encoding and fed to an ensemble network for classification. Compared to the existing MIL pooling methods and encoding schemes, the DGMIR-FV improves the model’s generalization ability by employing estimation network for instance relevance estimation and incorporating relevant instances in the encoding process. The experimental results demonstrate the efficiency of DGMIR-FV on several MIL benchmark datasets.

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References

  1. Zhou Z-H (2018) A brief introduction to weakly supervised learning. Natl Sci Rev 5(1):44–53

    Article  Google Scholar 

  2. Carbonneau M-A, Granger E, Gagnon G (2016) Witness identification in multiple instance learning using random subspaces. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 3639–3644

  3. Carbonneau M-A, Cheplygina V, Granger E, Gagnon G (2018) Multiple instance learning: a survey of problem characteristics and applications. Pattern Recogn 77:329–353

    Article  Google Scholar 

  4. Waqas M, Khan Z, Anjum S, Tahir MA (2020) Lung-wise tuberculosis analysis and automatic ct report generation with hybrid feature and ensemble learning. In: CLEF (Working notes)

  5. Hu S, Gao Y, Niu Z, Jiang Y, Li L, Xiao X, Wang M, Fang EF, Menpes-Smith W, Xia J et al (2020) Weakly supervised deep learning for covid-19 infection detection and classification from ct images. IEEE Access 8:118869–118883

    Article  Google Scholar 

  6. Andrews S, Tsochantaridis I, Hofmann T (2002) Support vector machines for multiple-instance learning. In: NIPS, vol 2, p 7

  7. Andrews S, Hofmann T (2003) Multiple instance learning via disjunctive programming boosting. Adv Neural Inf Process Syst 16

  8. Foulds J, Frank E (2010) A review of multi-instance learning assumptions. Knowl Eng Rev 25(1):1–25

    Article  Google Scholar 

  9. Sabato S, Tishby N (2012) Multi-instance learning with any hypothesis class. J Mach Learn Res 13(1):2999–3039

    MathSciNet  MATH  Google Scholar 

  10. Wei X-S, Wu J, Zhou Z-H (2016) Scalable algorithms for multi-instance learning. IEEE Trans Neural Netw Learn Syst 28(4):975–987

    Article  Google Scholar 

  11. Wang X, Yan Y, Tang P, Bai X, Liu W (2018) Revisiting multiple instance neural networks. Pattern Recogn 74:15–24

    Article  Google Scholar 

  12. Yuan L, Xu G, Zhao L, Wen X, Xu H (2020) Multiple-instance learning via multiple-point concept based instance selection. Int J Mach Learn Cybern 11(9):2113–2126

    Article  Google Scholar 

  13. Carbonneau M-A, Granger E, Raymond AJ, Gagnon G (2016) Robust multiple-instance learning ensembles using random subspace instance selection. Pattern Recogn 58:83–99

    Article  Google Scholar 

  14. Wei X-S, Wu J, Zhou Z-H (2014) Scalable multi-instance learning. In: 2014 IEEE international conference on data mining. IEEE, pp 1037–1042

  15. Waqas M, Tahir MA, Qureshi R (2021) Ensemble-based instance relevance estimation in multiple-instance learning. In: 2021 9th European workshop on visual information processing (EUVIP). IEEE, pp 1–6

  16. Zhou Z-H, Sun Y-Y, Li Y-F (2009) Multi-instance learning by treating instances as non-iid samples. In: Proceedings of the 26th annual international conference on machine learning, pp 1249–1256

  17. Ilse M, Tomczak J, Welling M (2018) Attention-based deep multiple instance learning. In: International conference on machine learning. PMLR, pp 2127–2136

  18. Zhou Z-H, Zhang M-L (2007) Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowl Inf Syst 11(2):155–170

    Article  Google Scholar 

  19. Asif A et al (2019) An embarrassingly simple approach to neural multiple instance classification. Pattern Recogn Lett 128:474–479

    Article  Google Scholar 

  20. Li XC, Zhan DC, Yang JQ, Shi Y (2021) Deep multiple instance selection. Sci China Inf Sci 64(3):130102

    Article  MathSciNet  Google Scholar 

  21. Shi X, Xing F, Xie Y, Zhang Z, Cui L, Yang L (2020) Loss-based attention for deep multiple instance learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 5742–5749

  22. Dietterich TG, Lathrop RH, Lozano-Pérez T (1997) Solving the multiple instance problem with axis-parallel rectangles. Artif Intell 89(1-2):31–71

    Article  MATH  Google Scholar 

  23. Cheplygina V, de Bruijne M, Pluim JP (2019) Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 54:280–296

    Article  Google Scholar 

  24. Liu B, Xiao Y, Hao Z (2018) A selective multiple instance transfer learning method for text categorization problems. Knowl-Based Syst 141:178–187

    Article  Google Scholar 

  25. Wang J, Zucker J-D (2000) Solving multiple-instance problem: a lazy learning approach. In: Proceedings of the seventeenth international conference on machine learning, (ICML), pp 1119–1126

  26. Chen Y, Wang JZ (2004) Image categorization by learning and reasoning with regions. J Mach Learn Res 5:913–939

    MathSciNet  Google Scholar 

  27. Chen Y, Bi J, Wang JZ (2006) Miles: multiple-instance learning via embedded instance selection. IEEE Trans Pattern Anal Mach Intell 28(12):1931–1947

    Article  Google Scholar 

  28. Hong R, Wang M, Gao Y, Tao D, Li X, Wu X (2013) Image annotation by multiple-instance learning with discriminative feature mapping and selection. IEEE Trans Cybern 44(5):669– 680

    Article  Google Scholar 

  29. Abro WA, Aicher A, Rach N, Ultes S, Minker W, Qi G (2022) Natural language understanding for argumentative dialogue systems in the opinion building domain. Knowl-Based Syst 242:108318. https://doi.org/10.1016/j.knosys.2022.108318

    Article  Google Scholar 

  30. Abro WA, Qi G, Aamir M, Ali Z (2022) Joint intent detection and slot filling using weighted finite state transducer and bert. Appl Intell:1–15

  31. Abro WA, Qi G, Ali Z, Feng Y, Aamir M (2020) Multi-turn intent determination and slot filling with neural networks and regular expressions. Knowl-Based Syst 208:106428

    Article  Google Scholar 

  32. Riaz MN, Shen Y, Sohail M, Guo M (2020) Exnet: an efficient approach for emotion recognition in the wild. Sensors 20(4): 1087

    Article  Google Scholar 

  33. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J Roy Stat Soc: Ser B (Methodol) 39(1):1–22

    MathSciNet  MATH  Google Scholar 

  34. Zong B, Song Q, Min MR, Cheng W, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International conference on learning representations

  35. Dorta G, Vicente S, Agapito L, Campbell ND, Simpson I (2018) Structured uncertainty prediction networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5477–5485

  36. Sánchez J, Perronnin F, Mensink T, Verbeek J (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. FAST School of Computing, National University of Computer & Emerging Science (NUCES), Karachi Campus, Karachi, 75030, Pakistan

    Muhammad Waqas & Muhammad Atif Tahir

  2. University College of Zhob, Balochistan University of IT, Engineering and Management Sciences, Quetta, Pakistan

    Muhammad Waqas

  3. Department of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, TX, USA

    Rizwan Qureshi

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  1. Muhammad Waqas
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  2. Muhammad Atif Tahir
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Waqas, M., Tahir, M.A. & Qureshi, R. Deep Gaussian mixture model based instance relevance estimation for multiple instance learning applications. Appl Intell 53, 10310–10325 (2023). https://doi.org/10.1007/s10489-022-04045-7

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