Histopathological Images Research Articles

Segmentation and Classification of Lung Cancer using Deep Learning Techniques

To increase the survival rates for lung cancer, early identification and diagnosis are essential. The detection becomes crucial for a number of reasons, including visual similarity, heterogeneity, and low contrast variation. The several varieties of lung cancer that can be categorised histologically are determined by the kind of cells a pathologist can detect under a microscope. 15% of lung cancer cases are caused by small cell lung cancer (SCLC), while 85% of cases are caused by non-small cell lung cancer (NSCLC). The three main types of NSCLC are lung adenocarcinoma, big cell carcinoma, and squamous cell carcinoma (SQCC). Deep learning models provides high level services to the healthcare domain. The proposed research focuses on applying deep learning models to segment and classify images of lung cancer histopathology. Images are segmented using the U-Net model. In order to categorise the different forms of lung cancer histopathology images, classification models like ResNet-50, VGG-16, and EfficientNet-B5 and Ensemble models of these three models were utilised. For the purpose of evaluating all the classifiers, performance metrics like accuracy, precision, recall, and F1 Score were computed. The U-Net model achieves 81% accuracy. ResNet-50 achieved an accuracy of 91%, VGG-16 94%, EfficientNet-B5 97%, and Ensemble model 99%, respectively. Among all models, an ensemble model is the most accurate.

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

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

MSGM: An Advanced Deep Multi-Size Guiding Matching Network for Whole Slide Histopathology Images Addressing Staining Variation and Low Visibility Challenges.

Matching whole slide histopathology images to provide comprehensive information on homologous tissues is beneficial for cancer diagnosis. However, the challenge arises with the Giga-pixel whole slide images (WSIs) when aiming for high-accuracy matching. Learning-based methods are difficult to generalize well with large-size WSIs, necessitating the integration of traditional matching methods to enhance accuracy as the size increases. In this paper, we propose a multi-size guiding matching method applicable high-accuracy requirements. Specifically, we design learning multiscale texture to train deep descriptors, called TDescNet, that trains 64 × 64 × 256 and 256 × 256 × 128 size convolution layer as C64 and C256 descriptors to overcome staining variation and low visibility challenges. Furthermore, we develop the 3D-ring descriptor using sparse keypoints to support the description of large-size WSIs. Finally, we employ C64, C256, and 3D-ring descriptors to progressively guide refined local matching, utilizing geometric consistency to identify correct matching results. Experiments show that when matching WSIs of size 4096 × 4096 pixels, our average matching error is 123.48 μm and the success rate is 93.02 % in 43 cases. Notably, our method achieves an average improvement of 65.52 μm in matching accuracy compared to recent state-of-the-art methods, with enhancements ranging from 36.27 μm to 131.66 μm. Therefore, we achieve high-fidelity whole-slice image matching, and overcome staining variation and low visibility challenges, enabling assistance in comprehensive cancer diagnosis through matched WSIs.

A Novel Colorectal Histopathological Image Classification Method Based on Progressive Multi-Granularity Feature Fusion of Patch

A Novel Colorectal Histopathological Image Classification Method Based on Progressive Multi-Granularity Feature Fusion of Patch

FINE-TUNING MobileNetV3 WITH DIFFERENT WEIGHT OPTIMIZATION ALGORITHMS FOR CLASSIFICATION OF DENOISED BLOOD CELL IMAGES USING CONVOLUTIONAL NEURAL NETWORK

Breast cancer remains a formidable global health concern, underscoring the urgency for advanced diagnostic methodologies. This research presents a multifaceted framework aimed at significantly enhancing breast cancer diagnosis through innovative approaches in image processing and machine learning. The proposed framework encompasses several key contributions. Firstly, a robust denoising strategy is implemented using Convolutional Neural Network encoder-decoder architecture, augmented with data augmentation techniques. This addresses the challenge of vanishing gradients through enhanced Rectified Linear Units based Convolutional Neural Network, enhancing the model's generalization capability. Subsequent to denoising, feature extraction is performed utilizing a fine-tuned MobileNetV3 model. The model's performance is optimized through Modified Rectified Linear Units and NRMSProp approaches, effectively eliminating undesired features and improving overall efficiency. Crucially, a novel feature selection process is introduced, leveraging the Artificial Hummingbird Algorithm based on Manta Ray Foraging Optimization Algorithm. This algorithm selectively identifies essential features from breast cancer images, significantly elevating classification accuracy. To validate the proposed framework, a comprehensive evaluation is conducted, comparing its performance with a hybrid of five different metaheuristic algorithms, including Marine Predators Algorithm, Tunicate Swarm Algorithm, Manta Ray Foraging Optimization algorithm, Arithmetic Optimization Algorithm, and Jelly Fish optimization algorithm. Artificial Hummingbird Algorithm based on Manta Ray Foraging Optimization Algorithm emerges as the most effective among these algorithms, showcasing superior performance. The evaluation utilized the Breast Cancer Histopathological Image Classification dataset, resulting in an impressive classification accuracy of 99.51% for the proposed model.

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary nodules is essential to slow down the degenerative process and reduce mortality. The interactive paradigm assisted by neural networks is considered to be an effective means for early lung cancer screening in large populations. However, some inherent characteristics of pulmonary nodules in high-resolution CT images, e.g., diverse shapes and sparse distribution over the lung fields, have been inducing inaccurate results. On the other hand, most existing methods with neural networks are dissatisfactory from a lack of transparency. In order to overcome these obstacles, a united framework is proposed, including the classification and feature visualization stages, to learn distinctive features and provide visual results. Specifically, a bilateral scheme is employed to synchronously extract and aggregate global-local features in the classification stage, where the global branch is constructed to perceive deep-level features and the local branch is built to focus on the refined details. Furthermore, an encoder is built to generate some features, and a decoder is constructed to simulate decision behavior, followed by the information bottleneck viewpoint to optimize the objective. Extensive experiments are performed to evaluate our framework on two publicly available datasets, namely, 1) the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and 2) the Lung and Colon Histopathological Image Dataset (LC25000). For instance, our framework achieves 92.98% accuracy and presents additional visualizations on the LIDC. The experiment results show that our framework can obtain outstanding performance and is effective to facilitate explainability. It also demonstrates that this united framework is a serviceable tool and further has the scalability to be introduced into clinical research.

Brain Tumor Detection Systems Based on Histopathological Image Analysis Using Segmentation and Classification by Deep Learning Architectures

Brain Tumor Detection Systems Based on Histopathological Image Analysis Using Segmentation and Classification by Deep Learning Architectures

Partial-Label Contrastive Representation Learning for Fine-grained Biomarkers Prediction from Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSI) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel partial-label contrastive representation learning approach to enhance the discrimination of histopathology image representations for fine-grained biomarkers prediction. We designed a partial-label contrastive clustering (PLCC) module for partial-label disambiguation and a dynamic clustering algorithm to sample the most representative features of each category to the clustering queue during the contrastive learning process. We conducted comprehensive experiments on three gene mutation prediction datasets, including USTC-EGFR, BRCA-HER2, and TCGA-EGFR. The results show that our method outperforms 9 existing methods in terms of Accuracy, AUC, and F1 Score. Specifically, our method achieved an AUC of 0.950 in EGFR mutation subtyping of TCGA-EGFR and an AUC of 0.853 in HER2 0/1+/2+/3+ grading of BRCA-HER2, which demonstrates its superiority in fine-grained biomarkers prediction from histopathology whole slide images. The source code is available at https://github.com/WkEEn/PLCC.

Brain tumour detection systems based on histopathological image analysis using segmentation and classification by deep learning architectures

Brain tumour detection systems based on histopathological image analysis using segmentation and classification by deep learning architectures

Breast Cancer Histopathology Images Classification Through Multi-View Augmented Contrastive Learning and Pre-Learning Knowledge Distillation

Breast Cancer Histopathology Images Classification Through Multi-View Augmented Contrastive Learning and Pre-Learning Knowledge Distillation

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis.

Lymph node metastasis is associated with a poorer prognosis in endometrial cancer. The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer. This study is a systematic review. We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST). A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer. A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed. None.

Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning

Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning

Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate.

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov-Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences.

Microsnoop: A generalist tool for microscopy image representation

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research. Here, we present Microsnoop, a novel deep learning-based representation tool trained on large-scale microscopy images using masked self-supervised learning. Microsnoop can process various complex and heterogeneous images, and we classified images into three categories: single-cell, full-field, and batch-experiment images. Our benchmark study on 10 high-quality evaluation datasets, containing over 2,230,000 images, demonstrated Microsnoop's robust and state-of-the-art microscopy image representation ability, surpassing existing generalist and even several custom algorithms. Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis. Furthermore, Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms. We will regularly retrain and reevaluate the model using community-contributed data to consistently improve Microsnoop.

Image-based Lung Analysis in the Context of Digital Pathology: a Brief Review

Lung cancer is the 2nd most diagnosed cancer worldwide. The corresponding histopathological analysis, being both costly and time-consuming, demands the commitment of skilled professionals who, while engrossed in this task, experience constraints on their ability to attend to other crucial responsibilities. Moreover, as it is a human-driven process, mistakes may lead to incorrect diagnosis and treatment. Given the disease frequency and mortality, automated diagnostic systems, using Artificial Intelligence (AI), represent valuable improvements in diagnostic timing and overall performance. Recently, Deep Learning (DL) has been widely used for extracting features from histopathologic images approaching more accurate and expeditious analysis. With this line of research in mind, a brief review of recent technical/scientific works within the scope of lung Digital Pathology (DP) image analysis is provided in this paper, covering different computer vision tasks including classification, segmentation, and detection. Furthermore, available datasets and open-source annotation tools capable of providing support to the aforementioned DP-related tasks are also overviewed. Afterward, a summary table and a discussion around the reviewed approaches is provided, consolidating critical information such as technique/DL architecture, involved datasets, metrics, etc. From this study, it was observed that the ARA-CNN technique achieved the highest Area Under the Curve (AUC) ranging from 0.72 to 0.99 for classification. On the other hand, the multimodal-based approach, with an AUC of 0.95, performed better for the segmentation task. As for the detection task, the BCNN approach stood out, achieving a high AUC of 0.988. This review work aims to provide a comprehensive overview of recent advancements in lung DP image analysis and serves as a foundation for future research in this critical area.

Fusion of ESRGAN, Adaptive NSCT, and Multi-Attention CNN With Wavelet Transform for Histopathological Image Classification

Fusion of ESRGAN, Adaptive NSCT, and Multi-Attention CNN With Wavelet Transform for Histopathological Image Classification

Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning.

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

An enhanced multi-scale deep convolutional orchard capsule neural network for multi-modal breast cancer detection

Breast cancer is the second-leading cause of cancer death in women. Breast cells develop into malignant, cancerous lumps, the first signs of breast cancer. Breast cancer can be discovered by the automated diagnostic system when it is still too little to be found by conventional medical methods. Early breast cancers identified with automated screening and diagnosis technologies are generally treatable. This study proposes an enhanced multi-scale deep Convolutional Capsule Neural Network (CapsNet) optimized with Orchard Optimization Algorithm for breast cancer detection. The proposed system consists of preprocessing, feature extraction, segmentation, and classification process. Two input images are taken initially: the Breast Cancer Histopathology Images dataset and the Infrared Thermal Images dataset. The quality of the collected data is improved, and unwanted noises are removed. The features are extracted to segment the image to derive a Region of Interest for effectively segmenting the affected region. Finally, the images are classified as benign/malignant for histopathology images and healthy/cancer for thermal images. The proposed CapsNet is implemented in Python, run for 200 epochs, and compared with existing methods in terms of evaluation metrics. The result shows that the proposed CapsNet attained 99.74 % accuracy, 0.0482 binary entropy loss on the Breast Cancer Histopathology Image dataset and 97 % accuracy, 0.2081 binary entropy loss on the Infrared Thermal Images dataset while incrementing the epochs at each level.

The Techniques Used in Mitosis Detection in Breast Cancer Histopathology Images: A Survey

Breast cancer consider as the second cause of death around the world after heart disease, and it is the primary cause of death for women. Timely detection of breast cancer plays a crucial role in lowering mortality rates, as it enhances the patient's prospects of survival through prompt diagnosis and appropriate treatment. The discovery of the mitotic number is one of the necessary procedures that must be performed for a person suffering from breast cancer because it is an important marker for determining the aggressiveness of the tumor. According to the Nottingham scale, it gives 3 degrees to determine the degree of the tumor, whether it is of the first degree, the second degree, or the third degree of seriousness. Deep learning algorithms have many contributions in the medical fields, including in the field of mitotic number discovery, as the mitotic number process is a difficult and tiring task that requires time and effort from pathologists (diagnostic doctors), because the work environment is under microscopes with high magnification degrees, for this reason deep learning techniques were used to reduce the burden on diagnostic doctors and save time for the patient to know the result of his examination, as the biopsy results in developed countries take from 10 days to two weeks for the results to appear. In this survey, we will evaluate the deep learning techniqus employed for mitotic number detection.

Recurrence risk stratification of hepatocellular carcinomas based on immune gene expression and features extracted from pathological images.

Immune-based therapy is a promising type of treatment for hepatocellular carcinoma (HCC) but has only been partially successful due to the high heterogeneity in HCC tumor. The differences in the degree of tumor cell progression and in the activity of tumor immune microenvironment could lead to varied clinical outcome. Accurate subgrouping for recurrence risk is an approach to address the issue of such heterogeneity. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome. We included a total of 231 patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project. For each patient, we extracted 18 statistical metrics corresponding to a global region of interest and 135 features regarding nucleus shape from WSI. A risk score was developed using these image features with high-dimensional survival modeling. We also introduced into the model the expression profile of 66 representative marker genes relevant to currently available immunotherapies. We stratified all patients into higher and lower-risk subgroup based on the final risk score selected from multiple models generated, and further investigated underlying molecular mechanisms associated with the risk stratification. One WSI feature and three immune marker genes were selected into the final recurrence-free survival (RFS) prediction model following the best integrated modeling framework. The resultant score showed a significantly improved prediction performance on the test dataset (mean time-dependent AUCs = 0.707) as compared to those of other types (e.g: mean time-dependent AUCs of AJCC tumor stage = 0.525) of input data integration. To assess that the risk score could provide a higher-resolution risk stratification, a lower-risk subgroup (or a higher-risk subgroup) was arbitrarily assigned according to score falling below (or above) the median score. The lower risk subgroup had significantly longer median RFS time than that of the higher-risk patients (median RFS = 903 vs. 265 days, log-rank test p-value< 0.0001). Additionally, the higher-risk subgroup, in contrast to the lower-risk patients were characterized with a significant downregulation of immune checkpoint genes, suppressive signal in tumor immune response pathways, and depletion of CD8 T cells. These observations for the higher-risk subgroup suggest that new targets for adoptive or checkpoint-based combined systemic therapies may be useful. We developed a novel prognostic model to predict RFS for HCC patients, using one feature that can be automatically extracted from routine histopathological images, as well as the expression profiles of three immune marker genes. The methodology used in this paper demonstrates the feasibility of developing prognostic models that provide both useful risk stratification along with valuable biological insights into the underlying characteristics of the subgroups identified.