Explaining instances in the health domain based on the exploration of a dataset's hardness embedding-Reference-Cited by-同舟云学术

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Explaining instances in the health domain based on the exploration of a dataset's hardness embedding

Published:2024-07-14 Issue: Volume:35 Page:1598-1606
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Valeriano Maria Gabriela¹²^ORCID,Pereira João Luiz Junho³^ORCID,Veiga Kiffer Carlos Roberto⁴^ORCID,Lorena Ana Carolina¹^ORCID

Affiliation:

1. Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil

2. Universidade Federal de São Paulo, São José dos Campos, Brazil

3. Instituto Tecnológico de Aeronáutica, São Jose dos Campos, Brazil

4. Universidade Federal de São Paulo, São Paulo, Brazil

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638530.3664113

Reference28 articles.

1. Revolutionizing Healthcare: How Deep Learning is poised to Change the Landscape of Medical Diagnosis and Treatment

2. David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Network Dissection: Quantifying Interpretability of Deep Visual Representations. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21--26, 2017. IEEE Computer Society, 3319--3327.

3. The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations

4. The effects of example-based explanations in a machine learning interface

5. "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

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