Clinical Characteristics and Local Histopathological Modulators of Endometriosis and Its Progression

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(3), С. 1789 - 1789

Опубликована: Фев. 1, 2024

Endometriosis (E) and adenomyosis (A) are associated with a wide spectrum of symptoms may present various histopathological transformations, such as the presence hyperplasia, atypia, malignant transformation occurring under influence local inflammatory, vascular hormonal factors by alteration tumor suppressor proteins inhibition cell apoptosis, an increased degree lesion proliferation. Material methods: This retrospective study included 243 patients from whom tissue E/A or normal control uterine was harvested stained histochemical classical immunohistochemical staining. We assessed symptomatology patients, structure ectopic epithelium neovascularization, hormone receptors, inflammatory cells oncoproteins involved in development. Atypical areas were analyzed using multiple immunolabeling techniques. Results: The cytokeratin (CK) CK7+/CK20− expression profile E foci differentiated them digestive metastases. neovascularization marker cluster differentiation (CD) 34+ increased, especially A foci. T:CD3+ lymphocytes, B:CD20+ CD68+ macrophages tryptase+ mast abundant, cases transformation, being markers proinflammatory microenvironment. In addition, we found significantly division index (Ki67+), inactivation genes p53, B-cell lymphoma 2 (BCL-2) Phosphatase tensin homolog (PTEN) E/A-transformed malignancy. Conclusions: Proinflammatory/vascular/hormonal changes trigger progression onset cellular atypia exacerbating symptoms, pain vaginal bleeding. These triggers represent future therapeutic targets.

Язык: Английский

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SwinCup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer

Expert Systems with Applications, Год журнала: 2022, Номер 216, С. 119452 - 119452

Опубликована: Дек. 30, 2022

Transformer models have recently become the dominant architecture in many computer vision tasks, including image classification, object detection, and segmentation. The main reason behind their success is ability to incorporate global context information into learning process. By utilising self-attention, recent advancements design enable consider long-range dependencies. In this paper, we propose a novel transformer, named Swin with Cascaded UPsampling (SwinCup) model for segmentation of histopathology images. We use hierarchical shifted windows as an encoder extract features. multi-scale feature extraction transformer enables attend different areas at scales. A cascaded up-sampling decoder used improve its aggregation. Experiments on GLAS CRAG colorectal cancer datasets were validate model, achieving average 0.90 (F1 score) surpassing state-of-the-art by (23%).

Язык: Английский

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A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms

Journal of Imaging, Год журнала: 2023, Номер 9(9), С. 173 - 173

Опубликована: Авг. 27, 2023

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is improve the diagnosis accuracy thyroid abnormality detection models that can be utilized alleviate undue pressure on healthcare professionals. In research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework detect abnormalities from ultrasound histopathological images. method uses three recently learning techniques (DeiT, Swin Transformer, Mixer-MLP) extract features image datasets. feature extraction are based Image Transformer MLP models. There large number redundant overfit classifiers reduce generalization capabilities classifiers. order avoid overfitting problem, six transformation (PCA, TSVD, FastICA, ISOMAP, LLE, UMP) analyzed dimensionality data. five different (LR, NB, SVC, KNN, RF) evaluated using 5-fold stratified cross-validation technique transformed dataset. Both datasets exhibit class imbalances hence, used evaluate performance. MEREC-TOPSIS for ranking at analysis stages. first stage, best classification chosen, whereas, second reduction wrapper selection mode. Two best-ranked further selected weighted average ensemble meta-heuristics FOX-optimization algorithm. PCA+FOX optimization-based + random forest model achieved highest TOPSIS score performed exceptionally well with an 99.13%, F2-score 98.82%, AUC-ROC 99.13% Similarly, 90.65%, 92.01%, 95.48% This study exploits combination novelty algorithms cancer capabilities. outperforms current state-of-the-art methods significantly medical professionals by reducing excessive burden fraternity.

Язык: Английский

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Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Diagnostics, Год журнала: 2024, Номер 14(4), С. 422 - 422

Опубликована: Фев. 14, 2024

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, advent deep learning techniques has showcased notable potential elevating precision efficiency data analysis. The proposed work introduces novel approach that harnesses power Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it nuanced landscape breast histopathology. Our model, Learning-based concatenated exhibits substantial performance enhancements compared traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, DenseNet121—each Convolutional Neural Network architecture designed for classification tasks—this study meticulously tunes hyperparameters optimize model performance. implementation is systematically benchmarked against individual classifiers data. Remarkably, our achieves an impressive training accuracy 98%. outcomes experiments underscore efficacy this four-level advancing By synergizing strengths transfer learning, holds augment diagnostic capabilities pathologists, thereby contributing more informed personalized planning individuals diagnosed with cancer. This research heralds promising stride toward leveraging cutting-edge technology refine understanding management cancer, marking advancement intersection artificial intelligence healthcare.

Язык: Английский

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Histopathology image classification: highlighting the gap between manual analysis and AI automation

Frontiers in Oncology, Год журнала: 2024, Номер 13

Опубликована: Янв. 17, 2024

The field of histopathological image analysis has evolved significantly with the advent digital pathology, leading to development automated models capable classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology tasks, including tumor identification, metastasis detection, patient prognosis assessment. However, traditional manual methods generally low accuracy diagnosing colorectal cancer using This study investigates use AI classification analytics images histogram oriented gradients method. develops an AI-based architecture for images, aiming achieve high performance less complexity through specific parameters layers. In this study, we investigate complicated state classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered datasets that included records 100.000 non-overlapping from 86 patients training 7180 50 testing. compares two approaches, artificial intelligence-based algorithms machine learning models, automate classification. comprises primary tasks: binary distinguishing between normal tissues, multi-classification, encompassing types, adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, colon mucosa, tumor. findings show systems can 0.91 0.97 multi-class classifications. comparison, directed gradient features Random Forest classifier achieved rates 0.75 0.44 classifications, respectively. are generalizable, allowing them be integrated into histopathology diagnostics procedures improve diagnostic efficiency. CNN model outperforms existing techniques, demonstrating its potential precision effectiveness analysis. emphasizes importance maintaining data consistency applying normalization during preparation stage It particularly highlights assess

Язык: Английский

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The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Diagnostics, Год журнала: 2024, Номер 14(4), С. 388 - 388

Опубликована: Фев. 10, 2024

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that workflow combines DP and artificial intelligence (AI) applied histopathology potential value supporting diagnosis, treatment evaluation, prognosis prediction diseases. Here, we provide systematic review use this field hepatology. Based on PRISMA 2020 criteria, search PubMed, SCOPUS, Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted specifications objectives each study, AI tools used, results obtained. From 266 initial records identified, 25 eligible selected, mainly conducted human tissues. Most performed using whole-slide imaging systems for acquisition different machine learning deep methods image pre-processing, segmentation, feature extractions, classification. Of note, most selected demonstrated good performance as classifiers histological images compared pathologist annotations. Promising date bode well not-too-distant inclusion these techniques clinical practice.

Язык: Английский

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CViTS-Net: A CNN-ViT Network With Skip Connections for Histopathology Image Classification

IEEE Access, Год журнала: 2024, Номер 12, С. 117627 - 117649

Опубликована: Янв. 1, 2024

Язык: Английский

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From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Cureus, Год журнала: 2024, Номер unknown

Опубликована: Апрель 27, 2024

Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis prognosis. Artificial intelligence (AI) emerges as transformative force in this realm, offering innovative solutions to enhance traditional methods. This narrative review explores AI's pioneering role colorectal carcinoma histopathology, encompassing its evolution, techniques, advancements. AI algorithms, notably machine learning deep learning, have revolutionized image analysis, facilitating accurate prognosis prediction. Furthermore, AI-driven analysis unveils potential biomarkers therapeutic targets, heralding personalized treatment approaches. Despite promise, challenges persist, including data quality, interpretability, integration. Collaborative efforts among researchers, clinicians, developers are imperative surmount these hurdles realize full care. underscores impact implications future oncology research, clinical practice, interdisciplinary collaboration.

Язык: Английский

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Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 321 - 334

Опубликована: Янв. 1, 2025

Язык: Английский

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

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