Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification

Haryanto, Toto; Suhartanto, Heru; Arymurthy, Aniati Murni; Kusmardi, Kusmardi

doi:10.1016/j.imu.2021.100565

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…This approach enabled the inference of smaller subsegments of fascicles of arbitrary lengths. Although the sliding window is a very popular technique for training processes on CNN [ 42 , 43 ], the novelty of this research is the combination of the approach to multiple LSTM units for fascicle length extraction. While the proposed model was trained on fixed-length sequences, no a priori information was provided in input to the model on the length of the inputs, suggesting that the model would successfully deal with varying-length inputs and maintain good performances across participants and tasks.…”

Section: Discussionmentioning

confidence: 99%

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

Gionfrida

Nuckols

Walsh

et al. 2023

Sensors

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In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R2 = 0.85, RMSE = 2.36 ± 1.51 mm, MAPE = 3.6%, aaDF = 0.48 ± 1.1 mm) and older adults (R2 = 0.53, RMSE = 4.7 ± 2.51 mm, MAPE = 5.19%, aaDF = 1.9 ± 1.39 mm). Then, the performances were trained across all ages (R2 = 0.79, RMSE = 3.95 ± 2.51 mm, MAPE = 4.5%, aaDF = 0.67 ± 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3–5%, suggesting that the error may be acceptable in the generation of assistive force profiles.

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Section: Discussionmentioning

confidence: 99%

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

Gionfrida

Nuckols

Walsh

et al. 2023

Sensors

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“…This section presents a hybrid methodology for diagnosing images of the breast cancer dataset and distinguishing between its benign and malignant types. Some biomedical datasets, including the BreakHis dataset, do not yield satisfactory results when classified by pre-trained deep learning models [41]. Therefore, this technique solves these challenges and achieves satisfactory results for diagnosing eight benign and malignant breast cancer types.…”

Section: Ann According To the Merge Of Deep Learning Featuresmentioning

confidence: 99%

“…In 2020, more than 2.3 million women were diagnosed with breast cancer, and 685,000 women died of breast cancer worldwide [2]. Over the past five years, more than 7.8 million women have been diagnosed with breast cancer, which Diagnostics 2023, 13,1753 2 of 41 indicates its increased risk. Breast cancer arises in the breast's glandular tissue, in the cells of the epithelium of the ducts 85% or the cells of the lobules 15%.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

et al. 2023

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Breast cancer is the second most common type of cancer among women, and it can threaten women’s lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient’s abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.

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“…In addition, the lack of sufficient data leads to overfitting problems during the training process. A conditional sliding windows arithmetic is proposed in Haryanto et al ( 2021 ) to solve this problem, which generates histopathological images. This arithmetic successfully solves the limitation of rectal histopathological data.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

Zhao

Rahaman

et al. 2022

Front. Microbiol.

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In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyper-parameters tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet, and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the vision transformer (ViT) model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

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Conditional sliding windows: An approach for handling data limitation in colorectal histopathology image classification

Cited by 13 publications

References 15 publications

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers

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