Building an Explainable Diagnostic Classification Model for Brain Tumor using Discharge Summaries

Nair, Priyanka C.; Gupta, Deepa; Devi, Bhagavatula Indira; Kanjirangat, Vani

doi:10.1016/j.procs.2023.01.182

Cited by 11 publications

(1 citation statement)

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“…When citing examples of explainability, we can refer to a review of the XAI literature of the last decade on healthcare by Loh et al [17], as well as some recent specific cases where it has been applied on medical ML models, for example: a diagnostic classification model for brain tumor detection [18], a multi-label classification of electrocardiograms [19], and a breast cancer survival model [20]. More examples for the medical imaging field using deep learning cancer detection models [21], and for skin cancer recognition are described [22].…”

Section: Explainability In Healthcarementioning

confidence: 99%

Improving Medical Data Annotation Including Humans in the Machine Learning Loop

Bobes-Bascarán

Mosqueira-Rey

Alonso-Ríos

2021

The 4th XoveTIC Conference

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At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge. Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved. Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning. The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases.

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