Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

The Coping Styles and Perception of Illness in Patients with Breast Cancer—Relation to Body Image and Type of Surgery

Breast cancer is considered one of the most frequent causes of morbidity and death in women.Individuals’response to information regarding health threats and illness can influence the adjustment of the treatment to existing conditions including the issues of non-completion of treatment or non-attendance at medical appointments.The study aimed to examine the relationship between illness perception,body image dissatisfaction and(mal)adaptive coping styles in breast cancer patients.A sample of 197 patients with diagnosed breast cancer hospitalized at the Center for Oncology and Radiology,Kragujevac,Serbia,was surveyed.The instruments included sociodemographic questionnaire,a Brief Illness Perception Questionnaire(BIPQ),a Body Image Scale(BIS),and a Mini-Mental Adjustment to Cancer Scale(Mini-MAС).Results showed that 52%of the variance of maladaptive coping style in women who underwent mastectomy was explained by the negative illness perception,while body image dissatisfaction reflected through this connection(CFI>.95,GFI>.95,RMSEA=.01,SRMR=.08).Similar results were found in patients with breast-conserving surgery but with lower percentage(36%)of variance explained(CFI>.95,GFI>.95,RMSEA<.02,SRMR<.05).If confirmed by further studies,these results would suggest that patients who underwent mastectomy tend to be more dissatisfied with their body image,have tendency to perceive illness as threatening and resort to maladaptive coping styles.However,any form of appearance modification,including breast-conserving surgery,carries the risk of body image dissatisfaction,and consequently the risk of maladaptive coping behaviors.Our results suggest that health professionals and public policies should put an additional focus on the assessment of the patient’s body image dissatisfaction,to improve the health and wellbeing of the affected women.

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data set.The novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

A Hybrid Learning Algorithm for Breast Cancer Diagnosis

In many fields, particularly that of health, the diagnosis of diseases is a very difficult task to carry out. Therefore, early detection of diseases using artificial intelligence tools can be of paramount importance in the medical field. In this study, we proposed an intelligent system capable of performing diagnoses for radiologists. The support system is designed to evaluate mammographic images, thereby classifying normal and abnormal patients. The proposed method (DiagBC for Breast Cancer Diagnosis) combines two (2) intelligent unsupervised learning algorithms (the C-Means clustering algorithm and the Gaussian Mixture Model) for the segmentation of medical images and an algorithm for supervised learning (a modified DenseNet) for the diagnosis of breast images. Ultimately, a prototype of the proposed system was implemented for the Magori Polyclinic in Niamey (Niger) making it possible to diagnose (or classify) breast cancer into two (2) classes: the normal class and the abnormal class.

Research progress of artificial intelligence in evaluating the efficacy of neoadjuvant chemotherapy for breast cancer

Breast cancer is a major oncological challenge for females worldwide.The incorporation of neoadjuvant chemotherapy into comprehensive management strategies for breast cancer underscores the importance of the precise prognostication of therapeutic efficacy.In clinical diagnostics,medical imaging has emerged as a critical tool for delineating the structural transformations within breast cancer tumors resulting from pharmacological interventions.The evolution of artificial intelligence(AI)technologies has precipitated the delineation and quantification of imaging-based phenotypic features,thereby translating these structural modifications into quantifiable data alterations.This analytical approach has led to the development of innovative biomarkers for enhancing the predictability of neoadjuvant chemotherapy outcomes.This study aimed to elucidate the instrumental role of AI technology in the prognosis of neoadjuvant chemotherapy efficacy in breast cancer through the analytical exploration of ultrasound,magnetic resonance imaging,and histopathological imagery,while envisaging prospective trajectories within this research domain.

Automation of immunohistochemical evaluation in breast cancer using image analysis

AIM:To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.METHODS:Breast tissue specimens from sixty cases were stained separately for estrogen receptor(ER),progesterone receptor(PR)and human epidermal growth factor receptor-2(HER-2/neu).All cases were assessed by manual grading as well as image analysis.The manual grading was performed by an experienced expert pathologist.To study inter-observer and intra-observer variations,we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer.We also took a second reading from the second observer to study intra-observer variations.Image analysis was carried out using in-house developed software(TissueQuant).A comparison of the results from image analysis and manual scoring of ER,PR and HER-2/neu was also carried out.RESULTS:The performance of the automated analysis in the case of ER,PR and HER-2/neu expressions was compared with the manual evaluations.The performance of the automated system was found to correlate well with the manual evaluations.The inter-observer variations were measured using Spearman correlation coefficient r and 95%confidence interval.In the case of ER expression,Spearman correlation r=0.53,in the case of PR expression,r=0.63,and in the case of HER-2/neu expression,r=0.68.Similarly,intra-observer variations were also measured.In the case of ER,PR and HER-2/neu expressions,r=0.46,0.66 and 0.70,respectively.CONCLUSION:The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast cancer.Grading images by pathologists is a time consuming and subjective task.Therefore,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data.In the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches individually.The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(LSTM)network for classification.The LSTM network is utilised to consider all patches of an image simultaneously for image grading.The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.

Magnetic resonance image-guided versus ultrasound-guided high-intensity focused ultrasound in the treatment of breast cancer

Image-guided high-intensity focused ultrasound (HIFU) has been used for more than ten years, primarily in the treatment of liver and prostate cancers. HIFU has the advantages of precise cancer ablation and excellent protection of healthy tissue. Breast cancer is a common cancer in women. HIFU therapy, in combination with other therapies, has the potential to improve both oncologic and cosmetic outcomes for breast cancer patients by providing a curative therapy that conserves mammary shape. Currently, HIFU therapy is not commonly used in breast cancer treatment, and efforts to promote the application of HIFU is expected. In this article, we compare different image-guided models for HIFU and reviewed the status, drawbacks, and potential of HIFU therapy for breast cancer.

SEGMENTATION AND CORRELATION OF OPTICAL COHERENCE TOMOGRAPHY AND X-RAY IMAGES FOR BREAST CANCER DIAGNOSTICS

Pre-operative X ray mammography and int raoperative X-ray specimen radiography are routinely used to identify breast cancer pathology.Recent advances in optical coherence tomography(OCT)have enabled its 1use for the intraoperative assessment of surgical margins during breast cancer surgery.While each modality offers distinct contrast of normal and pathological features,there is an essential need to correlate image based features between the two modalities to take adv antage of the diagnostic capabilities of each technique.We compare OCT to X-ray images of resected human breast tissue and correlate different tissue features between modalities for future use in real-tine intraoperative OCT imaging.X ray imaging(specimen radiography)is currently used during surgical breast cancer procedures to verify tumor margins,but cannot image tissue in situ.OCT has the potential to solve this problem by providing intrao-perative imaging of the resected specimen as well as the in situ tumor cavity.OCT and micro-CT(X-ray)images are automatically segmented using different computational approaches,and quantitatively compared to determine the ability of these algorithms to automat ically differentiate regions of adipose tissue from tumor.Furthermore,two-dimensional(2D)and three-dimensional(3D)results are compared.These correlations,combined with real-time intraoperative OCT,have the potential to identify possible regions of tumor within breast tissue which correlate to tumor regions identified previously on X-ray imaging(mammography or specimen radiography).

ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

IN SITU IMAGING OF BREAST CANCER CELLS USING GREEN SEMICONDUCTOR QUANTUM DOTS

The breast cancer is the most common cause of cancer death in women. To establish an early stage in situ imaging of breast cancer cells, green quantum dots （QDs） are used as a fluorescent signal generator. The QDs based imaging of breast cancer cells involves anti-HER2/neu antibody for labeling the over expressed HER2 on the surface of breast cancer cells. The complete assay involves breast cancer cells, biotin labeled antibody and streptavidin conjugated QDs. The breast cancer cells are grown in culture plates and exposed to the biotin labeled antibodies, and then exposed to streptavidin labeled QDs to utilize the strong and stable biotin-streptavidin interaction. Fluorescent images of the complete assay for breast cancer cells are evaluated on a microscope with a UV light source. Results show that the breast cancer cells in the complete assay are used as fluorescent cells with brighter signals compared with those labeled by the organic dye using similar parameters and the same number of cells.

Magnetic resonance imaging in breast cancer:A literature review and future perspectives

Early detection and diagnosis of breast cancer are essential for successful treatment. Currently mammography and ultrasound are the basic imaging techniques for the detection and localization of breast tumors. The low sensitivity and specificity of these imaging tools resulted in a demand for new imaging modalities and breast magnetic resonance imaging(MRI) has become increasingly important in the detection and delineation of breast cancer in daily practice. However, the clinical benefits of the use of pre-operative MRI in women with newly diagnosed breast cancer is still a matter of debate. The main additional diagnostic value of MRI relies on specific situations such as detecting multifocal, multicentric or contralateral disease unrecognized on conventional assessment(particularly in patients diagnosed with invasive lobular carcinoma), assessing the response to neoadjuvant chemotherapy, detection of cancer in dense breast tissue, recognition of an occult primary breast cancer in patients presenting with cancer metastasis in axillary lymph nodes, among others. Nevertheless, the development of new MRI technolo-gies such as diffusion-weighted imaging, proton spectroscopy and higher field strength 7.0 T imaging offer a new perspective in providing additional information in breast abnormalities. We conducted an expert literature review on the value of breast MRI in diagnosing and staging breast cancer, as well as the future potentials of new MRI technologies.

Breast cancer screening and early diagnosis in Chinese women

Breast cancer is the most common malignant tumor in Chinese women,and its incidence is increasing.Regular screening is an effective method for early tumor detection and improving patient prognosis.In this review,we analyze the epidemiological changes and risk factors associated with breast cancer in China and describe the establishment of a screening strategy suitable for Chinese women.Chinese patients with breast cancer tend to be younger than Western patients and to have denser breasts.Therefore,the age of initial screening in Chinese women should be earlier,and the importance of screening with a combination of ultrasound and mammography is stressed.Moreover,Chinese patients with breast cancers have several ancestry-specific genetic features,and aiding in the determination of genetic screening strategies for identifying high-risk populations.On the basis of current studies,we summarize the development of risk-stratified breast cancer screening guidelines for Chinese women and describe the significant improvement in the prognosis of patients with breast cancer in China.

Ultra-sensitive Nanoprobe Modified with Tumor Cell Membrane for UCL/MRI/PET Multimodality Precise Imaging of Triple-Negative Breast Cancer

Triple-negative breast cancer(TNBC)is a subtype of breast cancer in which the estrogen receptor and progesterone receptor are not expressed,and human epidermal growth factor receptor 2 is not amplified or overexpressed either,which make the clinical diagnosis and treatment very challenging.Molecular imaging can provide an effective way to diagnose TNBC.Upconversion nanoparticles(UCNPs),are a promising new generation of molecular imaging probes.However,UCNPs still need to be improved for tumor-targeting ability and biocompatibility.This study describes a novel probe based on cancer cell membrane-coated upconversion nanoparticles(CCm-UCNPs),owing to the low immunogenicity and homologous-targeting ability of cancer cell membranes,and modified multifunctional UCNPs.This probe exhibits excellent performance in breast cancer molecular classification and TNBC diagnosis through UCL/MRI/PET tri-modality imaging in vivo.By using this probe,MDA-MB-231 was successfully differentiated between MCF-7 tumor models in vivo.Based on the tumor imaging and molecular classification results,the probe is also expected to be modified for drug delivery in the future,contributing to the treatment of TNBC.The combination of nanoparticles with biomimetic cell membranes has the potential for multiple clinical applications.

Review of treatment assessment using DCE-MRI in breast cancer radiation therapy

As a noninvasive functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging(DCEMRI) is being used in oncology to measure properties of tumor microvascular structure and permeability. Studies have shown that parameters derived from certain pharmacokinetic models can be used as imaging biomarkers for tumor treatment response. The use of DCE-MRI for quantitative and objective assessment of radiation therapy has been explored in a variety of methods and tumor types. However, due to the complexity in imaging technology and divergent outcomes from different pharmacokinetic approaches, the method of using DCE-MRI in treatment assessment has yet to be standardized, especially for breast cancer. This article reviews the basic principles of breast DCE-MRI and recent studies using DCE-MRI in treatment assessment. Technical and clinical considerations are emphasized with specific attention to assessment of radiation treatment response.

Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

Using shape contexts method for registration of contra lateral breasts in thermal images

AIM: To achieve symmetric boundaries for left and right breasts boundaries in thermal images by registration. METHODS: The proposed method for registration consists of two steps. In the first step, shape context, an approach as presented by Belongie and Malik was applied for registration of two breast boundaries. The shape context is an approach to measure shape similarity. Two sets of finite sample points from shape contours of two breasts are then presented. Consequently, the correspondences between the two shapes are found. By finding correspondences, the sample point which has the most similar shape context is obtained. RESULTS: In this study, a line up transformation which maps one shape onto the other has been estimated in order to complete shape. The used of a thin plate spline permitted good estimation of a plane transformation which has capability to map unselective points from one shape onto the other. The obtained aligningtransformation of boundaries points has been applied successfully to map the two breasts interior points. Some of advantages for using shape context method in this work are as follows:(1) no special land marks or key points are needed;(2) it is tolerant to all common shape deformation; and(3) although it is uncomplicated and straightforward to use, it gives remarkably powerful descriptor for point sets significantly upgrading point set registration. Results are very promising. The proposed algorithm was implemented for 32 cases. Boundary registration is done perfectly for 28 cases.CONCLUSION: We used shape contexts method that is simple and easy to implement to achieve symmetric boundaries for left and right breasts boundaries in thermal images.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.