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Nomura Y, Hanaoka S, Hayashi N, Yoshikawa T, Koshino S, Sato C, Tatsuta M, Tanaka Y, Kano S, Nakaya M, Inui S, Kusakabe M, Nakao T, Miki S, Watadani T, Nakaoka R, Shimizu A, Abe O. Performance changes due to differences among annotating radiologists for training data in computerized lesion detection. Int J Comput Assist Radiol Surg 2024; 19:1527-1536. [PMID: 38625446 PMCID: PMC11329536 DOI: 10.1007/s11548-024-03136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. METHODS We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. RESULTS The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. CONCLUSIONS Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used.
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Affiliation(s)
- Yukihiro Nomura
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan.
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Saori Koshino
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Chiaki Sato
- Department of Radiology, Tokyo Metropolitan Bokutoh Hospital, Tokyo, Japan
| | - Momoko Tatsuta
- Department of Diagnostic Radiology, Kitasato University Hospital, Sagamihara, Kanagawa, Japan
| | - Yuya Tanaka
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shintaro Kano
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Moto Nakaya
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shohei Inui
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | | | - Takahiro Nakao
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Soichiro Miki
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Takeyuki Watadani
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryusuke Nakaoka
- Division of Medical Devices, National Institute of Health Sciences, Kawasaki, Kanagawa, Japan
| | - Akinobu Shimizu
- Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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2
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Shi J, Zhang K, Guo C, Yang Y, Xu Y, Wu J. A survey of label-noise deep learning for medical image analysis. Med Image Anal 2024; 95:103166. [PMID: 38613918 DOI: 10.1016/j.media.2024.103166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/15/2024]
Abstract
Several factors are associated with the success of deep learning. One of the most important reasons is the availability of large-scale datasets with clean annotations. However, obtaining datasets with accurate labels in the medical imaging domain is challenging. The reliability and consistency of medical labeling are some of these issues, and low-quality annotations with label noise usually exist. Because noisy labels reduce the generalization performance of deep neural networks, learning with noisy labels is becoming an essential task in medical image analysis. Literature on this topic has expanded in terms of volume and scope. However, no recent surveys have collected and organized this knowledge, impeding the ability of researchers and practitioners to utilize it. In this work, we presented an up-to-date survey of label-noise learning for medical image domain. We reviewed extensive literature, illustrated some typical methods, and showed unified taxonomies in terms of methodological differences. Subsequently, we conducted the methodological comparison and demonstrated the corresponding advantages and disadvantages. Finally, we discussed new research directions based on the characteristics of medical images. Our survey aims to provide researchers and practitioners with a solid understanding of existing medical label-noise learning, such as the main algorithms developed over the past few years, which could help them investigate new methods to combat with the negative effects of label noise.
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Affiliation(s)
- Jialin Shi
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
| | - Kailai Zhang
- Department of Networks, China Mobile Communications Group Co., Ltd., Beijing, China
| | - Chenyi Guo
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | | | - Yali Xu
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Wu
- Department of Electronic Engineering, Tsinghua University, Beijing, China
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3
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Wei Y, Deng Y, Sun C, Lin M, Jiang H, Peng Y. Deep learning with noisy labels in medical prediction problems: a scoping review. J Am Med Inform Assoc 2024; 31:1596-1607. [PMID: 38814164 PMCID: PMC11187424 DOI: 10.1093/jamia/ocae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/27/2024] [Accepted: 05/03/2024] [Indexed: 05/31/2024] Open
Abstract
OBJECTIVES Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. METHODS Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical/healthcare/clinical," "uncertainty AND medical/healthcare/clinical," and "noise AND medical/healthcare/clinical." RESULTS A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. DISCUSSION From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.
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Affiliation(s)
- Yishu Wei
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
- Reddit Inc., San Francisco, CA 16093, United States
| | - Yu Deng
- Center for Health Information Partnerships, Northwestern University, Chicago, IL 10611, United States
| | - Cong Sun
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
| | - Mingquan Lin
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Hongmei Jiang
- Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
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4
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Penso C, Frenkel L, Goldberger J. Confidence Calibration of a Medical Imaging Classification System That is Robust to Label Noise. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2050-2060. [PMID: 38224509 DOI: 10.1109/tmi.2024.3353762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
A classification model is calibrated if its predicted probabilities of outcomes reflect their accuracy. Calibrating neural networks is critical in medical analysis applications where clinical decisions rely upon the predicted probabilities. Most calibration procedures, such as temperature scaling, operate as a post processing step by using holdout validation data. In practice, it is difficult to collect medical image data with correct labels due to the complexity of the medical data and the considerable variability across experts. This study presents a network calibration procedure that is robust to label noise. We draw on the fact that the confusion matrix of the noisy labels can be expressed as the matrix product between the confusion matrix of the clean labels and the label noises. The method is based on estimating the noise level as part of a noise-robust training method. The noise level is then used to estimate the network accuracy required by the calibration procedure. We show that despite the unreliable labels, we can still achieve calibration results that are on a par with the results of a calibration procedure using data with reliable labels.
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5
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Jin R, Li X. Backdoor attack and defense in federated generative adversarial network-based medical image synthesis. Med Image Anal 2023; 90:102965. [PMID: 37804585 DOI: 10.1016/j.media.2023.102965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 07/12/2023] [Accepted: 09/11/2023] [Indexed: 10/09/2023]
Abstract
Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research and augment medical datasets. Training generative adversarial neural networks (GANs) usually require large amounts of training data. Federated learning (FL) provides a way of training a central model using distributed data while keeping raw data locally. However, given that the FL server cannot access the raw data, it is vulnerable to backdoor attacks, an adversarial by poisoning training data. Most backdoor attack strategies focus on classification models and centralized domains. It is still an open question if the existing backdoor attacks can affect GAN training and, if so, how to defend against the attack in the FL setting. In this work, we investigate the overlooked issue of backdoor attacks in federated GANs (FedGANs). The success of this attack is subsequently determined to be the result of some local discriminators overfitting the poisoned data and corrupting the local GAN equilibrium, which then further contaminates other clients when averaging the generator's parameters and yields high generator loss. Therefore, we proposed FedDetect, an efficient and effective way of defending against the backdoor attack in the FL setting, which allows the server to detect the client's adversarial behavior based on their losses and block the malicious clients. Our extensive experiments on two medical datasets with different modalities demonstrate the backdoor attack on FedGANs can result in synthetic images with low fidelity. After detecting and suppressing the detected malicious clients using the proposed defense strategy, we show that FedGANs can synthesize high-quality medical datasets (with labels) for data augmentation to improve classification models' performance.
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Affiliation(s)
- Ruinan Jin
- Computer Science Department, The University of British Columbia, BC, V6T 1Z4, Canada
| | - Xiaoxiao Li
- Electrical and Computer Engineering Department, The University of British Columbia, BC, V6T 1Z4, Canada.
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6
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Khanal B, Bhattarai B, Khanal B, Linte CA. Improving Medical Image Classification in Noisy Labels Using only Self-supervised Pretraining. DATA ENGINEERING IN MEDICAL IMAGING : FIRST MICCAI WORKSHOP, DEMI 2023, HELD IN CONJUNCTION WITH MICCAI 2023, VANCOUVER, BC, CANADA, OCTOBER 8, 2023, PROCEEDINGS. DEMI (WORKSHOP) (1ST : 2023 : VANCOUVER, B.C.) 2023; 14314:78-90. [PMID: 39144367 PMCID: PMC11321236 DOI: 10.1007/978-3-031-44992-5_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Noisy labels hurt deep learning-based supervised image classification performance as the models may overfit the noise and learn corrupted feature extractors. For natural image classification training with noisy labeled data, model initialization with contrastive self-supervised pretrained weights has shown to reduce feature corruption and improve classification performance. However, no works have explored: i) how other self-supervised approaches, such as pretext task-based pretraining, impact the learning with noisy label, and ii) any self-supervised pretraining methods alone for medical images in noisy label settings. Medical images often feature smaller datasets and subtle inter-class variations, requiring human expertise to ensure correct classification. Thus, it is not clear if the methods improving learning with noisy labels in natural image datasets such as CIFAR would also help with medical images. In this work, we explore contrastive and pretext task-based selfsupervised pretraining to initialize the weights of a deep learning classification model for two medical datasets with self-induced noisy labels-NCT-CRC-HE-100K tissue histological images and COVID-QU-Ex chest X-ray images. Our results show that models initialized with pretrained weights obtained from self-supervised learning can effectively learn better features and improve robustness against noisy labels.
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Affiliation(s)
- Bidur Khanal
- Center for Imaging Science, RIT, Rochester, NY, USA
| | | | - Bishesh Khanal
- NepAl Applied Mathematics and Informatics Institute for Research (NAAMII), Patan, Nepal
| | - Cristian A Linte
- Center for Imaging Science, RIT, Rochester, NY, USA
- Biomedical Engineering, RIT, Rochester, NY, USA
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7
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Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
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8
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Zhu M, Zhang L, Wang L, Li D, Zhang J, Yi Z. Robust co-teaching learning with consistency-based noisy label correction for medical image classification. Int J Comput Assist Radiol Surg 2023; 18:675-683. [PMID: 36437387 DOI: 10.1007/s11548-022-02799-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 11/17/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE Deep neural networks (DNNs) have made great achievements in computer-aided diagnostic systems, but the success highly depends on massive data with high-quality labels. However, for many medical image datasets, a considerable number of noisy labels are introduced by inter- and intra-observer variability, thus hampering DNNs' performance. To address this problem, a robust noisy label correction method with the co-teaching learning paradigm is proposed. METHODS The proposed method aims to reduce the effect of noisy labels by correcting or removing them. It consists of two modules. An adaptive noise rate estimation module is employed to calculate the dataset's noise rate, which is helpful to detect noisy labels but is usually unavailable in clinical applications. A consistency-based noisy label correction module aims to detect noisy labels and correct them to reduce the disturbance from noisy labels and exploit useful information in data. RESULTS Experiments are conducted on the public skin lesion dataset ISIC-2017, ISIC-2019, and our constructed thyroid ultrasound image dataset. The results demonstrate that the proposed method outperforms other noisy label learning methods in medical image classification tasks. It is also evaluated on the natural image dataset CIFAR-10 to show its generalization. CONCLUSION This paper proposes a noisy label correction method to handle noisy labels in medical image datasets. Experimental results show that it can self-adapt to different datasets and efficiently correct noisy labels, which is suitable for medical image classification.
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Affiliation(s)
- Minjuan Zhu
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China.
| | - Lituan Wang
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Dong Li
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Jianwei Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Zhang Yi
- College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
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9
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Bui V, Hsu LY, Chang LC, Sun AY, Tran L, Shanbhag SM, Zhou W, Mehta NN, Chen MY. DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation. Front Artif Intell 2022; 5:1059007. [PMID: 36483981 PMCID: PMC9723331 DOI: 10.3389/frai.2022.1059007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/03/2022] [Indexed: 01/25/2023] Open
Abstract
Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.
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Affiliation(s)
- Vy Bui
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Li-Yueh Hsu
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, United States,*Correspondence: Li-Yueh Hsu
| | - Lin-Ching Chang
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - An-Yu Sun
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States,Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Loc Tran
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States,Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Sujata M. Shanbhag
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Wunan Zhou
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Nehal N. Mehta
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Marcus Y. Chen
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, United States
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10
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Alali MH, Roohi A, Angizi S, Deogun JS. Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks. MICROMACHINES 2022; 13:1364. [PMID: 36014286 PMCID: PMC9415388 DOI: 10.3390/mi13081364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/10/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGB-colored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., <8:8>. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems.
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Affiliation(s)
- Mohammed H. Alali
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
- Department of Computer Science, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
| | - Arman Roohi
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Shaahin Angizi
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Jitender S. Deogun
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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11
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Nguyen TV, Dakka MA, Diakiw SM, VerMilyea MD, Perugini M, Hall JMM, Perugini D. A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Sci Rep 2022; 12:8888. [PMID: 35614106 PMCID: PMC9133021 DOI: 10.1038/s41598-022-12833-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/06/2022] [Indexed: 11/09/2022] Open
Abstract
Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI training. Data-centric, cross-silo federated learning represents a pathway forward for training on distributed medical datasets. Existing approaches typically require updates to a training model to be transferred to a central server, potentially breaching data privacy laws unless the updates are sufficiently disguised or abstracted to prevent reconstruction of the dataset. Here we present a completely decentralized federated learning approach, using knowledge distillation, ensuring data privacy and protection. Each node operates independently without needing to access external data. AI accuracy using this approach is found to be comparable to centralized training, and when nodes comprise poor-quality data, which is common in healthcare, AI accuracy can exceed the performance of traditional centralized training.
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Affiliation(s)
- T V Nguyen
- Presagen, Adelaide, SA, 5000, Australia. .,School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia.
| | - M A Dakka
- Presagen, Adelaide, SA, 5000, Australia.,School of Mathematical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
| | | | - M D VerMilyea
- Ovation Fertility, Austin, TX, 78731, USA.,Texas Fertility Center, Austin, TX, 78731, USA
| | - M Perugini
- Presagen, Adelaide, SA, 5000, Australia.,Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5000, Australia
| | - J M M Hall
- Presagen, Adelaide, SA, 5000, Australia.,Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, SA, 5005, Australia.,School of Physical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia
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