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RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis
We propose a novel transfer learning framework for pathological image analysis, the Response-based Cross-task Knowledge Distillation (RCKD), which improves the performance of the model by pretraining it on a large unlabeled dataset guided by a high-performance teacher model. RCKD first pretrains a s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669242/ https://www.ncbi.nlm.nih.gov/pubmed/38002403 http://dx.doi.org/10.3390/bioengineering10111279 |
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author | Kim, Hyunil Kwak, Tae-Yeong Chang, Hyeyoon Kim, Sun Woo Kim, Injung |
author_facet | Kim, Hyunil Kwak, Tae-Yeong Chang, Hyeyoon Kim, Sun Woo Kim, Injung |
author_sort | Kim, Hyunil |
collection | PubMed |
description | We propose a novel transfer learning framework for pathological image analysis, the Response-based Cross-task Knowledge Distillation (RCKD), which improves the performance of the model by pretraining it on a large unlabeled dataset guided by a high-performance teacher model. RCKD first pretrains a student model to predict the nuclei segmentation results of the teacher model for unlabeled pathological images, and then fine-tunes the pretrained model for the downstream tasks, such as organ cancer sub-type classification and cancer region segmentation, using relatively small target datasets. Unlike conventional knowledge distillation, RCKD does not require that the target tasks of the teacher and student models be the same. Moreover, unlike conventional transfer learning, RCKD can transfer knowledge between models with different architectures. In addition, we propose a lightweight architecture, the Convolutional neural network with Spatial Attention by Transformers (CSAT), for processing high-resolution pathological images with limited memory and computation. CSAT exhibited a top-1 accuracy of 78.6% on ImageNet with only 3M parameters and 1.08 G multiply-accumulate (MAC) operations. When pretrained by RCKD, CSAT exhibited average classification and segmentation accuracies of 94.2% and 0.673 mIoU on six pathological image datasets, which is 4% and 0.043 mIoU higher than EfficientNet-B0, and 7.4% and 0.006 mIoU higher than ConvNextV2-Atto pretrained on ImageNet, respectively. |
format | Online Article Text |
id | pubmed-10669242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106692422023-11-02 RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis Kim, Hyunil Kwak, Tae-Yeong Chang, Hyeyoon Kim, Sun Woo Kim, Injung Bioengineering (Basel) Article We propose a novel transfer learning framework for pathological image analysis, the Response-based Cross-task Knowledge Distillation (RCKD), which improves the performance of the model by pretraining it on a large unlabeled dataset guided by a high-performance teacher model. RCKD first pretrains a student model to predict the nuclei segmentation results of the teacher model for unlabeled pathological images, and then fine-tunes the pretrained model for the downstream tasks, such as organ cancer sub-type classification and cancer region segmentation, using relatively small target datasets. Unlike conventional knowledge distillation, RCKD does not require that the target tasks of the teacher and student models be the same. Moreover, unlike conventional transfer learning, RCKD can transfer knowledge between models with different architectures. In addition, we propose a lightweight architecture, the Convolutional neural network with Spatial Attention by Transformers (CSAT), for processing high-resolution pathological images with limited memory and computation. CSAT exhibited a top-1 accuracy of 78.6% on ImageNet with only 3M parameters and 1.08 G multiply-accumulate (MAC) operations. When pretrained by RCKD, CSAT exhibited average classification and segmentation accuracies of 94.2% and 0.673 mIoU on six pathological image datasets, which is 4% and 0.043 mIoU higher than EfficientNet-B0, and 7.4% and 0.006 mIoU higher than ConvNextV2-Atto pretrained on ImageNet, respectively. MDPI 2023-11-02 /pmc/articles/PMC10669242/ /pubmed/38002403 http://dx.doi.org/10.3390/bioengineering10111279 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Hyunil Kwak, Tae-Yeong Chang, Hyeyoon Kim, Sun Woo Kim, Injung RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis |
title | RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis |
title_full | RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis |
title_fullStr | RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis |
title_full_unstemmed | RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis |
title_short | RCKD: Response-Based Cross-Task Knowledge Distillation for Pathological Image Analysis |
title_sort | rckd: response-based cross-task knowledge distillation for pathological image analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669242/ https://www.ncbi.nlm.nih.gov/pubmed/38002403 http://dx.doi.org/10.3390/bioengineering10111279 |
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