Histopathological Image Classification Research Articles

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring

Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.

Classification of breast cancer histopathology images using a modified supervised contrastive learning method.

Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method leveraging both image-level labels and domain-specific augmentations to enhance model robustness. This approach integrates self-supervised pre-training with a two-stage supervised contrastive learning strategy. In the first stage, we employ a modified supervised contrastive loss that not only focuses on reducing false negatives but also introduces an elimination effect to address false positives. In the second stage, a relaxing mechanism is introduced that refines positive and negative pairs based on similarity, ensuring that only relevant image representations are aligned. We evaluate our method on the BreakHis dataset, which consists of breast cancer histopathology images, and demonstrate an increase in classification accuracy by 1.45% in the image level, compared to the state-of-the-art method. This improvement corresponds to 93.63% absolute accuracy, highlighting the effectiveness of our approach in leveraging properties of data to learn more appropriate representation space. The code implementation of this study is accessible on GitHub https://github.com/matinamehdizadeh/Breast-Cancer-Detection .

Efficient feature selection for histopathological image classification with improved multi-objective WOA

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

Improving thyroid cancer diagnosis with advanced deep learning techniques

The accurate diagnosis of thyroid carcinoma is a complex and critical challenge in medical practice due to the clinical and pathological diversity of thyroid tumors. This study explores the application of advanced deep learning models, specifically EfficientNet B4 and MobileNetV3, to enhance the classification of thyroid histopathological images. By leveraging transfer learning, these models were fine-tuned on a dataset of 7,272 images encompassing three types of carcinoma: Medullary, Papillary, and Vesicular. The performance of these models was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicate that both models are effective, with EfficientNet B4 demonstrating slightly superior accuracy, particularly in more challenging diagnostic scenarios. Despite these promising results, the study acknowledges limitations related to dataset size and variability, which could affect the generalizability of the models. To address these issues, future research should focus on expanding the dataset and incorporating additional diagnostic data, such as genetic and molecular markers, to improve model performance and clinical applicability. This study highlights the potential of deep learning models to significantly enhance the diagnostic accuracy of thyroid carcinomas, paving the way for more reliable and precise clinical decision-making.

Histopathological image classification of colorectal cancer using a novel supervised contrastive learning approach

Histopathological image classification of colorectal cancer using a novel supervised contrastive learning approach

Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

Ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer

Breast cancer (BC) is the most frequently occurring cancer disease observed in women after lung cancer. Out of different stages, invasive ductal BC causes maximum deaths in women. In this work, three deep learning (DL) models such as Vision Transformer (ViT), Convmixer, and Visual Geometry Group-19 (VGG-19) are implemented for the detection and classification of different breast cancer tumors with the help of Breast cancer histopathological (Break His) image database. The performance of each model is evaluated using an 80:20 training scheme and measured in terms of accuracy, precision, recall, loss, F1-score, and area under the curve (AUC). From the simulation result, ViT showed the best performance for binary classification of breast cancer tumors with accuracy, precision, recall, and F1-score of 99.89 %, 98.29 %, 98.29 %, and 98.29 %, respectively. Also, ViT showed the best performance in terms of accuracy (98.21 %), average Precision (89.84 %), recall (89.97 %), and F1-score (88.75) for eight class classifications. Moreover, we have also ensemble the ViT-Convmixer model and observed that the performance of the ensemble model is reduced as compared to the ViT model. We have also compared the performance of the proposed best model with other existing models reported by several research groups. The study will help find suitable models that will increase accuracy in early diagnoses of BC. We hope the study will also help to minimize human errors in the early diagnosis of this fatal disease and administer appropriate treatment. The proposed model may also be implemented for the detection of other diseases with improved accuracy.

Fusion model of gray level co-occurrence matrix and convolutional neural network faced for histopathological images.

The image recognition of cancer cells plays an important role in diagnosing and treating cancer. Deep learning is suitable for classifying histopathological images and providing auxiliary technology for cancer diagnosis. The convolutional neural network is employed in the classification of histopathological images; however, the model's accuracy may decrease along with the increase in network layers. Extracting appropriate image features is helpful for image classification. In this paper, different features of histopathological images are represented by extracting features of the gray co-occurrence matrix. These features are recombined into a 16 × 16 × 3 matrix to form an artificial image. The original image and the artificial image are fused by summing the softmax output. The histopathological images are divided into the training set, validation set, and testing set. Each training dataset consists of 1500 images, while the validation dataset and test dataset each consist of 500 images. The results indicate that the effectiveness of our fusion model is demonstrated through significant improvements in accuracy, precision, recall, and F1-score, with an average accuracy reaching 99.31%. This approach not only enhances the classification performance of tissue pathology images but also holds promise for advancing computer-aided diagnosis in cancer pathology.

Few-shot learning based histopathological image classification of colorectal cancer

Background Colorectal cancer is a prevalent and deadly disease worldwide, posing significant diagnostic challenges. Traditional histopathologic images classification is often inefficient and subjective. Although some histopathologists use computer-aided diagnosis (CAD) to improve efficiency, these methods depend heavily on extensive data and specific annotations, limiting their development. To address these challenges, this paper proposes a method based on few-shot learning. Methods This study introduces a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant categories. The model comprises modules for feature extraction, dimensionality reduction, and classification, trained using a combination of contrast loss and cross-entropy loss. In this paper, we detail the setup of hyperparameters:n-way, k-shot, β and the creation of support, query, and test datasets. Results Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per category. We documented the model’s loss, accuracy, and the confusion matrix of the results. Additionally, we employed the t-SNE algorithm to analyze and assess the model’s classification performance. Conclusion The proposed model demonstrates significant advantages in accuracy and minimal data dependency, performing robustly across all tested n-way, k-shot scenarios. It consistently achieved over 93% accuracy on comprehensive test datasets, including 1916 samples, confirming its high classification accuracy and strong generalization capability. Our research advances the use of few-shot learning in medical diagnostics, and also lays the groundwork for extending it to deal with rare, difficult-to-diagnose cases.

Transformer based on multi-scale local feature for colon cancer histopathological image classification

Colon cancer histopathological image recognition is an important method in the early screening of colon cancer. At present, most whole slide images (WSIs) classification models only consider the features of a single patch, and do not consider the features of the WSIs region. To expand the receptive field of the patches and obtain its surrounding pathological information, this paper proposes a transformer based on multi-scale local feature for colon cancer histopathological image classification model. The model samples patches based on farthest point sampling (FPS). A multi-scale grouping scheme is also proposed to capture WSI local regions. Then the classification labels, feature information and local area information of sampled patches are embedded into the transformer encoder. In addition, the focal loss function is adopted to solve the problem of unbalanced categories of colon cancer histopathological images. Experimental results demonstrate that our model surpasses competing methods in performance on both hospital colon cancer datasets and The Cancer Genome Atlas(TCGA) colon cancer datasets.

Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images

Recent developments have highlighted the critical role that computer-aided diagnosis (CAD) systems play in analyzing whole-slide digital histopathology images for detecting gastric cancer (GC). We present a novel framework for gastric histology classification and segmentation (GHCS) that offers modest yet meaningful improvements over existing CAD models for GC classification and segmentation. Our methodology achieves marginal improvements over conventional deep learning (DL) and machine learning (ML) models by adaptively focusing on pertinent characteristics of images. This contributes significantly to our study, highlighting that the proposed model, which performs well on normalized images, is robust in certain respects, particularly in handling variability and generalizing to different datasets. We anticipate that this robustness will lead to better results across various datasets. An expectation-maximizing Naïve Bayes classifier that uses an updated Gaussian Mixture Model is at the heart of the suggested GHCS framework. The effectiveness of our classifier is demonstrated by experimental validation on two publicly available datasets, which produced exceptional classification accuracies of 98.87% and 97.28% on validation sets and 98.47% and 97.31% on test sets. Our framework shows a slight but consistent improvement over previously existing techniques in gastric histopathology image classification tasks, as demonstrated by comparative analysis. This may be attributed to its ability to capture critical features of gastric histopathology images better. Furthermore, using an improved Fuzzy c-means method, our study produces good results in GC histopathology picture segmentation, outperforming state-of-the-art segmentation models with a Dice coefficient of 65.21% and a Jaccard index of 60.24%. The model’s interpretability is complemented by Grad-CAM visualizations, which help understand the decision-making process and increase the model’s trustworthiness for end-users, especially clinicians.

Semi-supervised lung adenocarcinoma histopathology image classification based on multi-teacher knowledge distillation.

In this study, we propose a semi-supervised learning scheme using a patch-based deep learning framework to tackle the challenge of high-precision classification of seven lung tumor growth patterns, despite having a small amount of labeled data in whole slide images (WSIs). This scheme aims to enhance generalization ability with limited data and reduce dependence on large amounts of labeled data. It effectively addresses the common challenge of high demand for labeled data in medical image analysis.

Approach. To address these challenges, the study employs a semi-supervised learning approach enhanced by a dynamic confidence threshold mechanism. This mechanism adjusts based on the quantity and quality of pseudo labels generated. This dynamic thresholding mechanism helps avoid the imbalance of pseudo-label categories and the low number of pseudo-labels that may result from a higher fixed threshold. Furthermore, the research introduces a multi-teacher knowledge distillation technique. This technique adaptively weights predictions from multiple teacher models to transfer reliable knowledge and safeguard student models from low-quality teacher predictions.

Main results. The framework underwent rigorous training and evaluation using a dataset of 150 WSIs, each representing one of the seven growth patterns. The experimental results demonstrate that the framework is highly accurate in classifying lung tumor growth patterns in histopathology images. Notably, the performance of the framework is comparable to that of fully supervised models and human pathologists. In addition, the framework's evaluation metrics on a publicly available dataset are higher than those of previous studies, indicating good generalizability.

Significance. This research demonstrates that a semi-supervised learning approach can achieve results comparable to fully supervised models and expert pathologists, thus opening new possibilities for efficient and cost-effective medical images analysis. The implementation of dynamic confidence thresholding and multi-teacher knowledge distillation techniques represents a significant advancement in applying deep learning to complex medical image analysis tasks. This advancement could lead to faster and more accurate diagnoses, ultimately improving patient outcomes and fostering the overall progress of healthcare technology.&#xD.

Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability

Ovarian cancer, a significant threat to women's health, demands innovative diagnostic approaches. This paper introduces a groundbreaking Computer-Aided Diagnosis (CAD) framework for the classification of ovarian cancer, integrating Vision Transformer (ViT) models and Local Interpretable Model-agnostic Explanations (LIME). ViT models, including ViT-Base-P16-224-In21K, ViT-Base-P16-224, ViT-Base-P32-384, and ViT-Large-P32-384, exhibit exceptional accuracy, precision, recall, and overall robust performance across diverse evaluation metrics. The incorporation of a stacked model further enhances overall performance. Experimental results, conducted on the UBC-OCEAN training and testing datasets, highlight the proficiency of ViT models in accurately classifying ovarian cancer subtypes based on histopathological images. ViT-Large-P32-384 stands out as a top performer, achieving 98.79% accuracy during training and 97.37% during testing. Visualizations, including Receiver Operating Characteristic (ROC) curves and Local Interpretable Model-agnostic Explanations (LIME), provide insights into discriminative capabilities and enhance model interpretability. The proposed CAD framework represents a significant advancement in ovarian cancer diagnostics, offering a promising avenue for accurate and transparent multi-class classification of histopathological images.

DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images.

Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed diagnosis easy. The classification model based on deep learning has made some progress on gastric histopathology images. However, traditional convolutional neural networks (CNNs) generally use pooling operations, which will reduce the spatial resolution of the image, resulting in poor prediction results. The image feature in previous CNN has a poor perception of details. Therefore, we design a dilated CNN with a late fusion strategy (DCNNLFS) for gastric histopathology image classification. The DCNNLFS model utilizes dilated convolutions, enabling it to expand the receptive field. The dilated convolutions can learn the different contextual information by adjusting the dilation rate. The DCNNLFS model uses a late fusion strategy to enhance the classification ability of DCNNLFS. We run related experiments on a gastric histopathology image dataset to verify the excellence of the DCNNLFS model, where the three metrics Precision, Accuracy, and F1-Score are 0.938, 0.935, and 0.959.

Breast cancer histopathology image classification using an ensemble of optimized pretrained models with a trainable ensemble strategy classifier

Breast cancer histopathology image classification using an ensemble of optimized pretrained models with a trainable ensemble strategy classifier

Learning generalizable AI models for multi-center histopathology image classification

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA’s potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.

Deep Learning-Based Liver Histopathology Image Classification: State-of-the-Art Techniques and Emerging Trends

This research investigates the application of deep learning techniques to enhance the diagnostic accuracy of liver tumour classification in collaboration with a prominent hospital in South India. By leveraging a carefully curated dataset of histopathological images, we evaluated the performance of several advanced deep learning architectures, including DenseNet 121, ResNet50, and VGG16. Our findings reveal that DenseNet121 outperformed the other models, achieving the highest accuracy in both training and testing phases, thus exceeding our predefined accuracy benchmarks. The superior performance of DenseNet121 is attributed to its dense connectivity, which facilitates improved feature and gradient propagation throughout the network. This study highlights the significant potential of AI-driven diagnostics in enhancing liver tumour classification, thereby optimizing the diagnostic workflow and providing substantial benefits for patient care and healthcare system efficiency.

Predictive breast cancer diagnosis using ensemble fuzzy model

Breast cancer continues to be a major global health challenge, necessitating reliable diagnostic methods for early detection and improved patient outcomes. This study introduces a novel ensemble fuzzy model for predictive breast cancer diagnosis, integrating multiple deep-learning classifiers with fuzzy logic to enhance decision-making. Traditional diagnostic approaches often struggle with the complexity and heterogeneity of breast cancer data, which this new model addresses through an innovative ensemble technique. The ensemble model combines the strengths of Inception-V4, Inception-ResNet, and Inception V3/V4 + BN, with fuzzy logic for adaptive priority assignment based on confidence scores. The method employs a re-parameterized Gompertz function to assign fuzzy ranks to constituent classifiers, allowing flexible fusion strategies. The proposed model is evaluated on two benchmark breast cancer datasets: the Digital Database for Screening Mammography (DDSM) and the Breast Cancer Histopathological Image Classification (BACH) dataset. It achieves high performance across key metrics, including accuracy, precision, recall, and F1 score, consistently outperforming individual classifiers. On the DDSM dataset, the ensemble fuzzy model attains an accuracy of 0.97, a recall of 0.93, a precision of 0.95, and an F1 score of 0.96. Similarly, on the BACH dataset, the proposed method records an accuracy of 97.05%, a recall of 99.31%, a precision of 95.44%, and an F1 score of 97.37%, demonstrating its robust capability to identify positive instances and maintain a balanced performance. These results highlight the potential of the ensemble fuzzy model to improve breast cancer diagnosis, offering a reliable solution to the inherent challenges in this field.

PATH-13. LEARNED RESIZING WITH EFFICIENT TRAINING (LRET) FACILITATES IMPROVED PERFORMANCE OF LARGE-SCALE BRAIN TUMOR HISTOLOGY IMAGE CLASSIFICATION MODELS

Abstract BACKGROUND Histologic examination is vital in oncology research and diagnostics. The adoption of digital scanning of whole slide images (WSI) has created an opportunity to leverage deep learning-based image classification methods to enhance diagnosis and risk stratification. However, technical limitations prevent training and deployment of accurate comprehensive multiclass deep convolutional neural networks (DCNN) models for histopathology image classification. The input dimensions of DCNN architectures are small compared to the typical pathologist field of view, degrading performance by excluding important architectural features. Furthermore, data requirements for comprehensive models are sufficiently large to overwhelm the system memory during training. METHODS A method termed Learned Resizing with Efficient Training (LRET) was developed to address the main limitations of traditional histopathology classification model training. The LRET method couples efficient training techniques with image resizing to facilitate seamless integration of larger histology image patches into state-of-the-art classification models while preserving important structural information. The LRET method was coupled with two distinct resizing techniques to train three diverse histology image datasets using five different DCNN architectures. Performance metrics were compared on cross validation and hold out test sets. RESULTS LRET-trained models were flexible to multiple input patch dimensions and DCNN models. We demonstrated performance improvement across all datasets while significantly reducing the training time and resources over traditional methods. Using a large-scale, multiclass brain tumor classification dataset consisting of 74 distinct histopathologic classes, LRET-trained models outperformed existing methods by 15-28% in accuracy, yielding 94% accuracy for the best model. CONCLUSION The LRET method for DCNN training significantly enhances the performance of large-scale multiclass histopathology image classification. The implications of this work extend to broader applications within medical imaging and beyond, where efficient integration of high-resolution images into deep learning pipelines is paramount for driving advancements research and clinical practice.

Explainable Histopathology Image Classification with Self-organizing Maps: A Granular Computing Perspective

AbstractThe automatic analysis of histology images is an open research field where machine learning techniques and neural networks, especially deep architectures, are considered successful tools due to their abilities in image classification. This paper proposes a granular computing methodology for histopathological image classification. It is based on embedding tiles of histopathology images using deep metric learning, where a self-organizing map is adopted to generate the granular structure in this learned embedding space. The SOM enables the implementation of an explainable mechanism by visualizing a knowledge space that the experts can use to analyze and classify the new images. Additionally, it provides confidence in the classification results while highlighting each important image fragment, with the benefit of reducing the number of false negatives. An exemplary case is when an image detail is indicated, with small confidence, as malignant in an image globally classified as benign. Another implemented feature is the proposal of additional labelled image tiles sharing the same characteristics to specify the context of the output decision. The proposed system was tested using three histopathology image datasets, obtaining the accuracy of the state-of-the-art black-box methods based on deep learning neural networks. Differently from the methodologies proposed so far for the same purpose, this paper introduces a novel explainable method for medical image analysis where the advantages of the deep learning neural networks used to build the embedding space for the image tiles are combined with the intrinsic explainability of the granular process obtained using the clustering property of a self-organizing map.