TY - GEN
T1 - Enabling Efficient Training of Convolutional Neural Networks for Histopathology Images
AU - Alali, Mohammed H.
AU - Roohi, Arman
AU - Deogun, Jitender S.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Convolutional Neural Networks (CNNs) have gained lots of attention in various digital imaging applications. They have proven to produce incredible results, especially on big data, that require high processing demands. With the increasing size of datasets, especially in computational pathology, CNN processing takes even longer and uses higher computational resources. Considerable research has been conducted to improve the efficiency of CNN, such as quantization. This paper aims to apply efficient training and inference of ResNet using quantization on histopathology images, the Patch Camelyon (PCam) dataset. An analysis for efficient approaches to classify histopathology images is presented. First, the original RGB-colored images are evaluated. Then, compression methods such as channel reduction and sparsity are applied. When comparing sparsity on grayscale with RGB modes, classification accuracy is relatively the same, but the total number of MACs is less in sparsity on grayscale by 77% than RGB. A higher classification result was achieved by grayscale mode, which requires much fewer MACs than the original RGB mode. Our method’s low energy and processing make this project suitable for inference on wearable healthcare low powered devices and mobile hospitals in rural areas or developing countries. This also assists pathologists by presenting a preliminary diagnosis.
AB - Convolutional Neural Networks (CNNs) have gained lots of attention in various digital imaging applications. They have proven to produce incredible results, especially on big data, that require high processing demands. With the increasing size of datasets, especially in computational pathology, CNN processing takes even longer and uses higher computational resources. Considerable research has been conducted to improve the efficiency of CNN, such as quantization. This paper aims to apply efficient training and inference of ResNet using quantization on histopathology images, the Patch Camelyon (PCam) dataset. An analysis for efficient approaches to classify histopathology images is presented. First, the original RGB-colored images are evaluated. Then, compression methods such as channel reduction and sparsity are applied. When comparing sparsity on grayscale with RGB modes, classification accuracy is relatively the same, but the total number of MACs is less in sparsity on grayscale by 77% than RGB. A higher classification result was achieved by grayscale mode, which requires much fewer MACs than the original RGB mode. Our method’s low energy and processing make this project suitable for inference on wearable healthcare low powered devices and mobile hospitals in rural areas or developing countries. This also assists pathologists by presenting a preliminary diagnosis.
KW - Computational Pathology
KW - Deep learning
KW - Quantization
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U2 - 10.1007/978-3-031-13321-3_47
DO - 10.1007/978-3-031-13321-3_47
M3 - Conference contribution
AN - SCOPUS:85135787990
SN - 9783031133206
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 533
EP - 544
BT - Image Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Revised Selected Papers
A2 - Mazzeo, Pier Luigi
A2 - Distante, Cosimo
A2 - Frontoni, Emanuele
A2 - Sclaroff, Stan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Image Analysis and Processing , ICIAP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -