52 research outputs found
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories
stain tissue slides that exhibit similar but not identical color appearance.
Due to this color shift between laboratories, convolutional neural networks
(CNNs) trained with images from one lab often underperform on unseen images
from the other lab. Several techniques have been proposed to reduce the
generalization error, mainly grouped into two categories: stain color
augmentation and stain color normalization. The former simulates a wide variety
of realistic stain variations during training, producing stain-invariant CNNs.
The latter aims to match training and test color distributions in order to
reduce stain variation. For the first time, we compared some of these
techniques and quantified their effect on CNN classification performance using
a heterogeneous dataset of hematoxylin and eosin histopathology images from 4
organs and 9 pathology laboratories. Additionally, we propose a novel
unsupervised method to perform stain color normalization using a neural
network. Based on our experimental results, we provide practical guidelines on
how to use stain color augmentation and stain color normalization in future
computational pathology applications.Comment: Accepted in the Medical Image Analysis journa
ΠΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ ΠΏΡΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠ°ΠΊΠ΅: ΠΎΠ±Π·ΠΎΡ
The study objective: the study objective is to examine the use of artificial intelligence (AI) in the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) and discuss the future potential of AI in CRC. Material and Methods. The Web of Science, Scopus, PubMed, Medline, and eLIBRARY databases were used to search for the publications. A study on the application of Artificial Intelligence (AI) to the diagnosis, treatment, and prognosis of Colorectal Cancer (CRC) was discovered in more than 100 sources. In the review, data from 83 articles were incorporated. Results. The review article explores the use of artificial intelligence (AI) in medicine, specifically focusing on its applications in colorectal cancer (CRC). It discusses the stages of AI development for CRC, including molecular understanding, image-based diagnosis, drug design, and individualized treatment. The benefits of AI in medical image analysis are highlighted, improving diagnosis accuracy and inspection quality. Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. Ethical and regulatory considerations in integrating AI are mentioned, including patient trust, data security, and liability in AI-assisted surgeries. The review emphasizes the importance of an AI standard system, dataset standardization, and integrating clinical knowledge into AI algorithms. Overall, the article provides an overview of the current research on AI in CRC diagnosis, treatment, and prognosis, discussing its benefits, challenges, and future prospects in improving medical outcomes.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - ΠΎΡΠ΅Π½ΠΊΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° (ΠΠ) Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ° (ΠΠ Π ), Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΠ Π² Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΠ Π . ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΠΏΠΎΠΈΡΠΊ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ Π² ΠΏΠΎΠΈΡΠΊΠΎΠ²ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Web of Science, Scopus, PubMed, Medline ΠΈ eLIBRARY. ΠΡΠ»ΠΎ ΠΏΡΠΎΡΠΌΠΎΡΡΠ΅Π½ΠΎ Π±ΠΎΠ»Π΅Π΅ 100 ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΏΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ, Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠ Π . Π ΠΎΠ±Π·ΠΎΡ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΈΠ· 83 ΡΡΠ°ΡΠ΅ΠΉ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ, ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π½ΠΎΠΉ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° Π² ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Π΅, ΠΎΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ Π΅Π³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠΌ ΡΠ°ΠΊΠ΅. ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΡΡΠ°ΠΏΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ ΠΏΡΠΈ ΠΠ Π , Π²ΠΊΠ»ΡΡΠ°Ρ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ, Π»ΡΡΠ΅Π²ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΡ Π»Π΅ΠΊΠ°ΡΡΡΠ² ΠΈ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ΅ Π»Π΅ΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠ΄ΡΠ΅ΡΠΊΠ½ΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΠ Π² Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΠΠ’, ΠΠ Π’ ΠΈ ΠΠΠ’, ΡΡΠΎ ΠΏΠΎΠ²ΡΡΠ°Π΅Ρ ΡΠΎΡΠ½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ. Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΠ°ΠΊΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ, ΠΊΠ°ΠΊ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΡΠ΅ΠΌΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°Π΅ΡΡΡ ΡΠΎΠ»Ρ ΠΠ Π² Π²ΡΠ±ΠΎΡΠ΅ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²Π°. Π£ΡΠΈΡΡΠ²Π°ΡΡΡΡ ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠ΅ Π°ΡΠΏΠ΅ΠΊΡΡ ΠΠ, Π²ΠΊΠ»ΡΡΠ°Ρ Π΄ΠΎΠ²Π΅ΡΠΈΠ΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΡ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΡ Π² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΠ. ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΠ Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅, Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»ΠΎΡΠ΅ΠΊΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ°, ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΈ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π»Π΅ΡΠ΅Π½ΠΈΡ
Disentangled Autoencoder for Cross-Stain Feature Extraction in Pathology Image Analysis
A novel deep autoencoder architecture is proposed for the analysis of histopathology images. Its purpose is to produce a disentangled latent representation in which the structure and colour information are confined to different subspaces so that stain-independent models may be learned. For this, we introduce two constraints on the representation which are implemented as a classifier and an adversarial discriminator. We show how they can be used for learning a latent representation across haematoxylin-eosin and a number of immune stains. Finally, we demonstrate the utility of the proposed representation in the context of matching image patches for registration applications and for learning a bag of visual words for whole slide image summarization
Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors
Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning.
Methods: There are three major components of the I GRAD frameworkβimage segmentation, feature extraction, and clinical decision support.
Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset.
Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis.
Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables.
Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87Β±0.05 and 0.84Β±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence.
Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.
Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field
Deep Functional Mapping For Predicting Cancer Outcome
The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network.
In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents
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