The prediction of tumor progression and chemotherapy response has been recently tackled exploiting Tumor Infiltrating Lymphocytes (TILs) and the nuclear protein Ki67 as prognostic factors. Recently, deep neural networks (DNNs) have been shown to achieve top results in estimating Ki67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. However, in the last ten years the extraordinary progress induced by deep models proliferated at least as much as their resource demand. The exorbitant computational costs required to query (and in some cases also to store) a deep model represent a strong limitation in resource-limited contexts, like that of IoT-based applications to support healthcare personnel. To this end, we propose a resource consumption-aware DNN for the effective estimate of the percentage of Ki67-positive cells in breast cancer screenings. Our approach reduced up to 75% and 89% the usage of memory and disk space respectively, up to 1.5x the energy consumption, and preserved or improved the overall accuracy of a benchmark state-of-the-art solution. Encouraged by such positive results, we developed and structured the adopted framework so as to allow its general purpose usage, along with a public software repository to support its usage.

Resource-Limited Automated Ki67 Index Estimation in Breast Cancer / J. Gliozzo, G. Marinò, A. Bonometti, M. Frasca, D. Malchiodi - In: ICBRA '23: Proceedings[s.l] : ACM, 2024. - ISBN 979-8-4007-0815-2. - pp. 165-172 (( Intervento presentato al 10. convegno International Conference on Bioinformatics Research and Applications tenutosi a Bercelona nel 2023 [10.1145/3632047.3632072].

Resource-Limited Automated Ki67 Index Estimation in Breast Cancer

J. Gliozzo
Primo
;
A. Bonometti;M. Frasca
Penultimo
;
D. Malchiodi
Ultimo
2024

Abstract

The prediction of tumor progression and chemotherapy response has been recently tackled exploiting Tumor Infiltrating Lymphocytes (TILs) and the nuclear protein Ki67 as prognostic factors. Recently, deep neural networks (DNNs) have been shown to achieve top results in estimating Ki67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. However, in the last ten years the extraordinary progress induced by deep models proliferated at least as much as their resource demand. The exorbitant computational costs required to query (and in some cases also to store) a deep model represent a strong limitation in resource-limited contexts, like that of IoT-based applications to support healthcare personnel. To this end, we propose a resource consumption-aware DNN for the effective estimate of the percentage of Ki67-positive cells in breast cancer screenings. Our approach reduced up to 75% and 89% the usage of memory and disk space respectively, up to 1.5x the energy consumption, and preserved or improved the overall accuracy of a benchmark state-of-the-art solution. Encouraged by such positive results, we developed and structured the adopted framework so as to allow its general purpose usage, along with a public software repository to support its usage.
tumor infiltrating lymphocytes; Ki67 protein; resource-limited learning; resource-limited devices; DNN compression; deep learning
Settore INF/01 - Informatica
   Multi-criteria optimized data structures: from compressed indexes to learned indexes, and beyond
   MINISTERO DELL'ISTRUZIONE E DEL MERITO
   2017WR7SHH_004
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1034131
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