Histopathological Image Analysis Research Articles

Enhancing breast cancer classification via histopathological image analysis: Leveraging self-supervised contrastive learning and transfer learning

Breast cancer, a significant threat to women's health, demands early detection. Automating histopathological image analysis offers a promising solution to enhance efficiency and accuracy in diagnosis. This study addresses the challenge of breast cancer histopathological image classification by leveraging the ResNet architecture, known for its depth and skip connections. In this work, two distinct approaches were pursued, each driven by unique motivations. The first approach aimed to improve the learning process through self-supervised contrastive learning. It utilizes a small subset of the training data for initial model training and progressively expands the training set by incorporating confidently labeled data from the unlabeled pool, ultimately achieving a reliable model with limited training data. The second approach focused on optimizing the architecture by combining ResNet50 and Inception module to get a lightweight and efficient classifier. The dataset utilized in this work comprises histopathological images categorized into benign and malignant classes at varying magnification levels (40X, 100X, 200X, 400X), all originating from the same source image. The results demonstrate state-of-the-art performance, achieving 98% accuracy for images magnified at 40X and 200X, and 94% for 100X and 400X. Notably, the proposed architecture boasts a substantially reduced parameter count of approximately 3.6 million, contrasting with existing leading architectures, which possess parameter sizes at least twice as large.

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.

GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation.

Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large-scale annotated datasets for histopathological image analysis. However, in several scenarios, the availability of large-scale annotated data is a bottleneck while training such models. Self-supervised learning (SSL) is an alternative paradigm that provides some respite by constructing models utilizing only the unannotated data which is often abundant. The basic idea of SSL is to train a network to perform one or many pseudo or pretext tasks on unannotated data and use it subsequently as the basis for a variety of downstream tasks. It is seen that the success of SSL depends critically on the considered pretext task. While there have been many efforts in designing pretext tasks for classification problems, there have not been many attempts on SSL for histopathological image segmentation. Motivated by this, we propose an SSL approach for segmenting histopathological images via generative diffusion models. Our method is based on the observation that diffusion models effectively solve an image-to-image translation task akin to a segmentation task. Hence, we propose generative diffusion as the pretext task for histopathological image segmentation. We also utilize a multi-loss function-based fine-tuning for the downstream task. We validate our method using several metrics on two publicly available datasets along with a newly proposed head and neck (HN) cancer dataset containing Hematoxylin and Eosin (H&E) stained images along with annotations.

Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment.

Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence-assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. Areas under the curves obtained were 0.73 (95% confidence interval, 0.59-0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73-0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. Results indicate that machine learning with the integration of artificial intelligence-driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.

Position-based anchor optimization for point supervised dense nuclei detection

Nuclei detection is one of the most fundamental and challenging problems in histopathological image analysis, which can localize nuclei to provide effective computer-aided cancer diagnosis, treatment decision, and prognosis. The fully-supervised nuclei detector requires a large number of nuclei annotations on high-resolution digital images, which is time-consuming and needs human annotators with professional knowledge. In recent years, weakly-supervised learning has attracted significant attention in reducing the labeling burden. However, detecting dense nuclei of complex crowded distribution and diverse appearances remains a challenge. To solve this problem, we propose a novel point-supervised dense nuclei detection framework that introduces position-based anchor optimization to complete morphology-based pseudo-label supervision. Specifically, we first generate cellular-level pseudo labels (CPL) for the detection head via a morphology-based mechanism, which can help to build a baseline point-supervised detection network. Then, considering the crowded distribution of the dense nuclei, we propose a mechanism called Position-based Anchor-quality Estimation (PAE), which utilizes the positional deviation between an anchor and its corresponding point label to suppress low-quality detections far from each nucleus. Finally, to better handle the diverse appearances of nuclei, an Adaptive Anchor Selector (AAS) operation is proposed to automatically select positive and negative anchors according to morphological and positional statistical characteristics of nuclei. We conduct comprehensive experiments on two widely used benchmarks, MO and Lizard, using ResNet50 and PVTv2 as backbones. The results demonstrate that the proposed approach has superior capacity compared with other state-of-the-art methods. In particularly, in dense nuclei scenarios, our method can achieve 95.1% performance of the fully-supervised approach. The code is available at https://github.com/NucleiDet/DenseNucleiDet.

Automated Peripheral Smear Recognition and Classification of LGL Leukemia Using Machine Learning

Background: Machine learning can augment clinical decision support, especially in cases where complex diagnostic criteria are necessary. In making a diagnosis of LGL leukemia, a pathologist must review the peripheral smear thoroughly, which is a tedious process. Supervised machine learning has been successfully implemented in such cases by training on very large image-based datasets, followed by a corrective phase of updating the model weights and finally demonstrating a high degree of successful predictions in an independent dataset. We sought to assess whether a supervised machine learning model can automate histopathological image analysis and provide a degree of confidence in supporting a pathologist's manual review. Methods: We searched the published literature from 2016-2022 for high-definition peripheral smear images with confirmed cases. The samples were downloaded, pre-processed with the Segment Anything Model (SAM), and labeled as training, testing, or validation data. Non-pathological controls, defined as peripheral smears confirmed negative for LGLL, were also obtained from published literature and labeled manually. The neural network was written and trained on TensorFlow using the open-source Keras library with parallel threading and on a multicore GPU machine. The resulting predictive model had a binary classification task: Does the presented image have cells that resemble LGL cells, returning true or false. Every “true” label returned from the classifier also creates a stack of probable positives that need pathology review. Feedback from each pathologist review further updates the model weights to increase the probability of successful prediction in the future. Results: We screened 57 publications to obtain 163 high-definition peripheral smear images that had confirmed LGLL and 100 non-pathological smears. Two cohorts were created: 200 smears in a training cohort comprised of 100 LGLL smears and 100 non-pathological smears, and 63 smears in the validation cohort. To remain agnostic towards image orientation, we created 7 views of each smear, and 1400 total images underwent feature extraction using SAM. The resulting data was labeled and used for model training and our process is depicted visually below in Figure 1. During validation, our model accurately predicted LGL-cells in 56/63 cases with an 8% false positive rate, 2% false negatives and 2% unclassifiable. After updating model weights and re-training with Adaptive Boosting, the accuracy increased to 60/63 smears with a false-positive rate of 3%. Our trained classification model achieved an AUC of 0.8144. Conclusion: Supervised machine learning model with Adaptive Boosting can be used to label a peripheral smear image with a high likelihood of containing LGL cells and, in turn, help a pathologist review smears with higher priority in a timely fashion. Our initial training data was limited, which hindered the performance of our model; however, with a sufficiently large training dataset, we can drastically improve our accuracy. A more extensive training set with robust hardware can transform our image-recognition model into a foundational model where other clinical parameters can be added, and the final diagnostic probability will be based on multiple data streams. Our work takes a meaningful step in this direction, and future work at Karmanos Cancer Center will focus on extending our classifier to more sophisticated deep-learning algorithms. Our open-source platform will eventually support several niches in the future that are currently only served by commercial applications.

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene expressions among neighboring sites. In this paper, we develop TCGN (Transformer with Convolution and Graph-Node co-embedding method) for gene expression estimation from H&E-stained pathological slide images. TCGN comprises a combination of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone. Notably, TCGN uniquely operates with just a single spot image as input for histopathological image analysis, simplifying the process while maintaining interpretability. We validate TCGN on three publicly available spatial transcriptomic datasets. TCGN consistently exhibited the best performance (with median PCC 0.232). TCGN offers superior accuracy while keeping parameters to a minimum (just 86.241 million), and it consumes minimal memory, allowing it to run smoothly even on personal computers. Moreover, TCGN can be extended to handle bulk RNA-seq data while providing the interpretability. Enhancing the accuracy of omics information prediction from pathological images not only establishes a connection between genotype and phenotype, enabling the prediction of costly-to-measure biomarkers from affordable histopathological images, but also lays the groundwork for future multi-modal data modeling. Our results confirm that TCGN is a powerful tool for inferring gene expressions from histopathological images in precision health applications.

An analysis of Heptoprotective effects of plant extract on albino rats by Deep Learning analysis using ResNet and VGG16 algorithms

The hepatoprotective and anti-inflammatory effects of Terminalia catappa L. and Pergularia daemia hydroalcoholic extracts in albino rats with CCl4-induced liver damage (HAETC and HAEPD, respectively). The extracts significantly reduced the harm induced by CCl4 when given orally at a dosage of 200mg/kg. They also showed strong hepatoprotective and antioxidant properties. Wistar albino rats in good health were divided into seven groups, each with six animals. Group I acted as the control and received no treatment, whereas Group II was given 30% CCl4 (1ml/kg, intraperitoneally) to cause hepatotoxicity. After administering Silymarin (25 mg/kg, orally) as per protocol, Group III underwent CCl4 therapy. The CCl4-induced rats showed significant reductions in antioxidant enzymes like SOD, catalase, glutathione peroxidase (GPX), and glutathione-S-transferase (GST), as well as increases in serum marker enzymes like SGOT, SGPT, alkaline phosphatase (ALK), ACP, LDH, and lipid peroxidation (LPO) activity. However, rats given HAEPD and HAETC showed significant improvements, returning these parameters to levels that are almost normal (P 0.001).The hepatoprotective and antioxidant benefits of Terminalia catappa L. (HAETC) and Pergularia daemia (HAEPD) against CCl4-induced hepatotoxicity in rats were further validated by histopathological analysis of liver tissues. It encourages the use of pre-trained CNN architectures, such as VGG16 and ResNet (Residual Network), in the proposed method, which uses convolutional neural networks (CNNs) for histopathological image analysis to assess the hepatoprotective properties of Pergularia daemia and Terminalia catappa L. leaf extracts against CCl4-induced hepatotoxicity. These well-known CNN models may be modified for precise and effective histopathological image interpretation, assisting in the evaluation of the impact of plant extracts on liver health.

Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques.

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

An integrated approach for magnification independent breast cancer classification

Breast cancer (BC) is the second most common cancer worldwide, and its diagnosis and treatment rely heavily on histopathology image analysis. Moreover, continuous evaluation of deep learning techniques in medical imaging modalities is essential for improving early diagnosis. The Break-His dataset is the de facto standard for such analysis. Researchers have actively explored various approaches to classifying these images into binary, four, and eight based on the magnification-independent (MI) and magnification-dependent (MD). However, existing processes necessitate separate models for each classification based on the magnification factor and entail training multiple models. It leads to increased training time and model complexity. Moreover, model performance in eight-class is relatively low compared to other classifications. This paper introduces a modified Swin-Transformer V2 architecture for multi-labeled and eight-class classification on BC histopathology images. Further, the eight-class output was reconfigured to operate in an integrated approach, allowing the model to predict binary, four, and eight classes simultaneously for both MI and MD. On the Break-His dataset, the proposed model achieved an accuracy of 98.27%, 97.95%, 98.97%, and 99.16% for eight-class, malignant, benign, and binary classification tasks, respectively. Additionally, the multi-labeled approach demonstrated a performance improvement in the eight-class category compared to current leading models, achieving an enhancement of up to 5%, and the model was trained in just 2 h. Overall, the proposed model can improve the prognosis of breast cancer patients by leading to more accurate diagnosis and treatment. The implemented code can be accessed at https://github.com/kbnarayanavit/integrated-approach.

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis.

Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60-70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.

Harnessing the power of optical microscopy for visualization and analysis of histopathological images.

Histopathology is the foundation and gold standard for identifying diseases, and precise quantification of histopathological images can provide the pathologist with objective clues to make a more convincing diagnosis. Optical microscopy (OM), an important branch of optical imaging technology that provides high-resolution images of tissue cytology and structural morphology, has been used in the diagnosis of histopathology and evolved into a new disciplinary direction of optical microscopic histopathology (OMH). There are a number of ex-vivo studies providing applicability of different OMH approaches, and a transfer of these techniques toward in vivo diagnosis is currently in progress. Furthermore, combined with advanced artificial intelligence algorithms, OMH allows for improved diagnostic reliability and convenience due to the complementarity of retrieval information. In this review, we cover recent advances in OMH, including the exploration of new techniques in OMH as well as their applications, and look ahead to new challenges in OMH. These typical application examples well demonstrate the application potential and clinical value of OMH techniques in histopathological diagnosis.

Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison

The advent of whole slide imaging has brought advanced computer-aided diagnosis via medical imaging and artificial intelligence technologies in digital pathology. The examination of tissue samples through whole slide imaging is commonly used to diagnose cancerous diseases, but the analysis of histopathology images through a decision support system is not always accurate due to variations in color caused by different scanning equipment, staining methods, and tissue reactivity. These variabilities decrease the accuracy of computer-aided diagnosis and affect the diagnosis of pathologists. In this context, an effective stain normalization method has proved as a powerful tool to standardize different color appearances and minimize color variations in histopathology images. This study reviews different stain normalization methods highlighting the main methodologies, contributions, advantages, and limitations of correlated works. The state-of-the-art methods are grouped into four distinct categories. Next, we select ten representative methods from the groups and conduct an experimental comparison to investigate the strengths and weaknesses of different methods and rank them according to selected performance accuracy measures. The quality performances of selected methods are compared in terms of quaternion structure similarity index metric, structural similarity index metric, and Pearson correlation coefficient conducting experiments on three histopathological image datasets. Our findings conclude that the structure-preserving unified transformation-based methods consistently outperform the state-of-the-art methods by improving robustness against variability and reproducibility. The comparative analysis we conducted in this paper will serve as the basis for future research, which will help to refine existing techniques and develop new approaches to address the complexities of stain normalization in complex histopathology images.

Recurrence Network Analysis of Histopathological Images for the Detection of Invasive Ductal Carcinoma in Breast Cancer.

The histopathological image analysis is one of the most crucial diagnostic procedures to identify Invasive ductal carcinoma (IDC) in breast cancers. However, this diagnosis process is currently time-consuming and heavily dependent on human expertise. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for identifying IDC. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. We first utilize wavelet decomposition to delineate the subtle information in the images. Then, we model the patches with a weighted recurrence network approach to characterize the recurrence patterns of the histopathological images. Finally, we develop automated IDC detection models leveraging machine learning methods with spatial recurrence features extracted. The developed recurrence analysis models successfully characterize the complex microstructures of histopathological images and achieve the IDC detection performances of at least AUC = 0.96. This research developed a spatial recurrence analysis methodology to effectively identify IDC regions in histopathological images for BC. It shows a high potential to assist physicians in the decision-making process. The proposed methodology can further be applicable to image processing for other medical or biological applications.

Non-annotated renal histopathological image analysis with deep ensemble learning.

Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients' survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage. In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using the pre-trained CNNs and lastly perform the deep ensemble learning to determine the final classification. The heterogeneous ensemble models consisted of CNN models from five deep learning architectures, namely VGG, ResNet, DenseNet, MobileNet, and EfficientNet. These CNN models were fine-tuned and used as base learners, they exhibited different performances and had great diversity in histopathological image analysis. The CNN models with superior classification accuracy (Acc) were then selected to undergo ensemble learning for the final classification. The performance of the investigated ensemble approaches was evaluated against the state-of-the-art literature. The performance evaluation demonstrated the superiority of the proposed best performing ensembled model: five-CNN based weighted averaging model, with an Acc (99%), specificity (Sp) (98%), F1-score (F1) (99%) and area under the receiver operating characteristic (ROC) curve (98%) but slightly inferior recall (Re) (99%) compared to the literature. The outstanding robustness of the developed ensemble model with a superiorly high-performance scores in the evaluated metrics suggested its reliability as a diagnosis system for assisting the pathologists in analyzing the renal histopathological tissues. It is expected that the proposed ensemble deep CNN models can greatly improve the early detection of renal cancer by making the diagnosis process more efficient, and less misdetection and misdiagnosis; subsequently, leading to higher patients' survival rate.

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers.

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Learning how to detect: A deep reinforcement learning method for whole-slide melanoma histopathology images.

Cutaneous melanoma represents one of the most life-threatening malignancies. Histopathological image analysis serves as a vital tool for early melanoma detection. Deep neural network (DNN) models are frequently employed to aid pathologists in enhancing the efficiency and accuracy of diagnoses. However, due to the paucity of well-annotated, high-resolution, whole-slide histopathology image (WSI) datasets, WSIs are typically fragmented into numerous patches during the model training and testing stages. This process disregards the inherent interconnectedness among patches, potentially impeding the models' performance. Additionally, the presence of excess, non-contributing patches extends processing times and introduces substantial computational burdens. To mitigate these issues, we draw inspiration from the clinical decision-making processes of dermatopathologists to propose an innovative, weakly supervised deep reinforcement learning framework, titled Fast medical decision-making in melanoma histopathology images (FastMDP-RL). This framework expedites model inference by reducing the number of irrelevant patches identified within WSIs. FastMDP-RL integrates two DNN-based agents: the search agent (SeAgent) and the decision agent (DeAgent). The SeAgent initiates actions, steered by the image features observed in the current viewing field at various magnifications. Simultaneously, the DeAgent provides labeling probabilities for each patch. We utilize multi-instance learning (MIL) to construct a teacher-guided model (MILTG), serving a dual purpose: rewarding the SeAgent and guiding the DeAgent. Our evaluations were conducted using two melanoma datasets: the publicly accessible TCIA-CM dataset and the proprietary MELSC dataset. Our experimental findings affirm FastMDP-RL's ability to expedite inference and accurately predict WSIs, even in the absence of pixel-level annotations. Moreover, our research investigates the WSI-based interactive environment, encompassing the design of agents, state and reward functions, and feature extractors suitable for melanoma tissue images. This investigation offers valuable insights and references for researchers engaged in related studies. The code is available at: https://github.com/titizheng/FastMDP-RL.

A Shearlet-Based Second Order System for Classifying Oral Cancer: An Analysis of Histopathological Images

A Shearlet-Based Second Order System for Classifying Oral Cancer: An Analysis of Histopathological Images

Comparative Study of Deep Learning Algorithms for Classification of Lung Cancer Types from Histopathological Images

Abstract: Global cancer statistics shows that Lung cancer is the 2nd most common cancer and the leading cause of cancerrelated mortality worldwide in 2020.Histopathology image analysis act as the prominent technique for cancer diagnosis. The assessment of whole-slide histopathology images remains a limiting factor for timely treatments. The automation of histopathology analysis is a much-needed solution to alleviate the burden of workload and tackle the problem of medical personnel scarcity in underserved regions and populations. To help make the classification of cancer-type techniques less formidable to at least a certain extent, this report presents a comparative analysis of deep learning algorithms for the efficient classification of lung cancer types. We have compared six different pre-trained Convolutional neural networks (CNN) to find the one best suitable for the classification of lung cancer types. The models include ResNet50 , VGG-16 , EfficientNet-B0, InceptionV3 , DenseNet121, and NasNetLarge . The models were trained using histopathology images from dataset LC25000. 4 different evaluation metrics, which are accuracy, precision, recall, and F1-score were used. We could achieve the highest accuracy using EfficientNetB0, which was 99.77%, while ResNet50 yielded an accuracy of 99.66% and VGG-16 gave an accuracy of 93.50%. Overall, we can say that this research can aid in implementing models that can increase the efficiency and accuracy in the lung cancer type classification in biomedical fields.

A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolution neural network.

Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods.