Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Lear...
Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Contents
Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This st...
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Full title
Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images
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TN_cdi_doaj_primary_oai_doaj_org_article_85d86adf42df4e36b0ddb2acadc42d6c
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_85d86adf42df4e36b0ddb2acadc42d6c
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ISSN
2075-4418
E-ISSN
2075-4418
DOI
10.3390/diagnostics13203244
