Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Pham, Tuan D.

doi:10.1038/s41598-021-93160-5

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…The long-term dependency induces a vanishing gradient, which becomes negligible to allow the updating of the network weights. Applications of LSTM networks have reportedly been useful for classifying physiological signals [43] , [44] , [45] and histopathological images [46] .…”

Section: Methodsmentioning

confidence: 99%

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Pham

Ravi

Fan

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Background: Over a decade, tissues dissected adjacent to primary tumors have been considered ''normal'' or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers. Methods: This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs. Results: Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods. Conclusion: Preliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients. Clinical impact: The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.

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Section: Methodsmentioning

confidence: 99%

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Pham

Ravi

Fan

et al. 2023

IEEE J. Transl. Eng. Health Med.

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“…The images captured from the same area at different times can be considered time-series data. For instance, the CT images of the same body area scan at 1, 3 and 6 months, respectively [175] . The set of images can be regarded as time-series images data that contain abundant temporal relevant diagnostic information.…”

Section: Longitudinal Images Datamentioning

confidence: 99%

“…The set of images can be regarded as time-series images data that contain abundant temporal relevant diagnostic information. Integrating temporal information into medical imaging learning has significance for enhancing the diagnosis, prognosis, and disease progression analysis [175][176][177] . Some previous works used the CNN and recurrent neural network to mine the temporal and spatial information simultaneously [176,178] .…”

Section: Longitudinal Images Datamentioning

confidence: 99%

“…However, with our investigation, only a few works are involved in employing a pre-training approach in this field. Xu et al [175] demonstrated using the deep learning method to predict prognostic endpoints of patients treated with radiation on longitudinal CT imaging obtained follow-up. In this work, they use ImageNet pre-trained model to extract CT image features.…”

Section: Longitudinal Images Datamentioning

confidence: 99%

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Pre-training in Medical Data: A Survey

et al. 2023

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Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.

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“…Interesting approaches [67,70,88] were developed using either the U-Net model, where the initial image was encoded to a low resolution and then decoded, providing images with similar characteristics or the ShuffleNet [80,91,103]. Finally, other well-known models were also used, such as AlexNet [57], the YOLO detector [75], the CiFar Model [25], the DenseNet [73], the MobileNet [94], LSTM [71], Xception [51], the DarkNet [48] and EfficientNetB1 [62].…”

Section: Popular Architectures With Transfer Learningmentioning

confidence: 99%

Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review

et al. 2022

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Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.

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Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Cited by 7 publications

References 31 publications

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

Pre-training in Medical Data: A Survey

Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review

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