@article{(Open Science Index):https://publications.waset.org/pdf/10013578,
	  title     = {Attention Multiple Instance Learning for Cancer Tissue Classification in Digital Histopathology Images},
	  author    = {Afaf Alharbi and  Qianni Zhang},
	  country	= {},
	  institution	= {},
	  abstract     = {The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper presents a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network-based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation on an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.},
	    journal   = {International Journal of Medical and Health Sciences},
	  volume    = {18},
	  number    = {3},
	  year      = {2024},
	  pages     = {70 - 74},
	  ee        = {https://publications.waset.org/pdf/10013578},
	  url   	= {https://publications.waset.org/vol/207},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 207, 2024},
	}