Digital Pathology‐Based Comparison of PyRadiomics and HistomicsTK for Nuclei Classification in Melanoma Whole Slide Images

The histological identification of papillary thyroid carcinoma (PTC) is straightforward for experienced endocrine pathologists. The increase in radical thyroidectomies led to a raise in the rate of postoperative incidental subcentimeter PTC foci and the recent introduction of the Non-Invasive Follicular Thyroid Neoplasm with Papillary-like Nuclear Features (NIFTP) as a less aggressive mimicker of PTC, which significantly complicated the histology screening of thyroid histology specimens. Artificial Intelligence (AI) applied to Whole Slide Images (WSI) can speed up these processes, aiding pathologists to improve diagnostic accuracy and turnaround times. Here we present a computational pathology pipeline for the identification of Microscopic foci of papillary Thyroid Carcinoma-like nuclear features using Artificial intelligence (MiThyCA). This algorithm relies on a tandem architecture consisting of a Convolutional Neural Network (CNN) designed to identify neoplastic areas within thyroid specimens, and a Vision Transformer (TinyViT) focused on detecting PTC-like areas within the neoplastic regions identified by the first model. The study was conducted on a multi-institutional cohort of 73 WSIs from 67 patients with normal thyroid tissue (n = 22 patients, 33%), NIFTP (n = 19, 28%), PTC (n = 23, 34%), and lymph nodes (n = 3, 5%). Cases were divided into training (n = 40 patients, 41 WSIs), validation (n = 13 patients, 14 WSIs) and test (n = 14 patients, 18 WSIs) sets. Each model singly demonstrated excellent performance at the tile-level on the validation set (accuracy = 0.95 and AUC-ROC = 0.95 for CNN, accuracy = 0.86 and AUC-ROC = 0.84 for TinyViT), with their tandem combination in MiThyCA showing accuracy = 0.85 and F1 score = 0.8 on the validation set at the whole WSI-level. The average total execution time of MiThyCA on the test set WSIs was 51 ± 27s on average on workstations not equipped with GPU, and up to 16 ± 6s and 11 ± 4s per WSI with Nvidia GPU and Apple's laptop chip, respectively. Worthy of note, WSIs dimension did not significantly impact the algorithm processing time. Given its speed and accessibility, MiThyCA is a promising AI-based computer-aided diagnostic tool for the detection of subcentimeter PTC foci in histology.

The tissue sample analysis based on the visualization of the entire tissue sample represents an important methodology in clinical diagnostics, research, and education. This process that enables visualization of the entire tissue sample is named whole slide imaging. For clinical purposes, professional and very expensive scanners are used for tissue sample digitization and the creation of whole slide images. In education, it is possible to use whole slide images that do not necessarily follow strict medical standards and regulations. Therefore, the important research task is to propose a methodology for the manual creation of whole slide images and its adequate use in medical education. In this paper, the use of manually created whole slide images in histology classes education was analyzed. In the experimental part of this research, the methodology of manual creation and usage of whole slide images (WSI) in histology education is presented in more detail. The subjective assessment of the user's satisfaction with WSI usage in education was also performed. It was shown that the whole slide images were evaluated very positively in the histological analysis and education process. Using WSI can significantly improve histology teaching, especially when distance education principles are implemented. Therefore, in crises like the pandemic situation COVID-19 WSI usage becomes a very important educational tool for teaching microscope-related topics. The results of this research showed that the use of whole slide images in histology classes is in line with the process of digital transformation in education.

Background/Objectives: Multiple instance learning (MIL) is widely used for automated classification of epithelial ovarian cancer subtypes from whole slide images (WSIs), but the relative contributions of feature extractor, aggregation strategy, and classification framework (flat vs. hierarchical) choices remain unclear under severe class imbalance. Methods: We evaluated 36 configurations on 510 WSIs from the UBC-OCEAN dataset using stratified five-fold cross-validation, comparing three pathology foundation models (Phikon-v2, CTransPath, UNI), six aggregators (mean/max pooling, ABMIL, CLAM-SB, DSMIL, DTP-TransMIL), and two classification strategies. Pathologist-annotated WSIs assessed attention map interpretability. Results: Feature extractor selection contributed substantially more variance than aggregator choice. Cascade balanced accuracy ranged from 0.538 (Phikon-v2) to 0.925 (UNI); CTransPath (~32 K pretraining WSIs) reached 0.870, exceeding Phikon-v2 (~58 K WSIs) and approaching UNI (~100 K+ WSIs), indicating that pretraining objective and architecture contribute as substantially as scale. The hierarchical cascade consistently improved high-grade serous carcinoma (HGSC) recall across all six evaluated configurations (+0.073 to +0.530), detecting 206 of 217 cases (0.949) with UNI max pooling. Quantitative spatial alignment analysis confirmed that both stronger feature extractors—CTransPath and UNI—generated significantly more spatially structured attention distributions than Phikon-v2 (paired Wilcoxon, p = 0.008 and p = 0.032, respectively). Conclusions: Feature extractor choice contributed more variance than aggregator selection, with the largest gap between Phikon-v2 and stronger extractors. Hierarchical cascades consistently improved HGSC recall across all configurations.

Feature representations of histopathology whole slide images (WSIs) are crucial to the downstream applications for computer-aided cancer diagnosis, including whole slide image classification, region of interest detection, hash retrieval, prognosis analysis, and other high-level inference tasks. State-of-the-art methods for whole slide image feature extraction generally rely on supervised learning algorithms based on fine-grained manual annotations, unsupervised learning algorithms without annotation, or directly use pre-trained features. At present, there is a lack of research on weakly supervised feature learning methods that only utilize WSI-level labeling. In this paper, we propose a weakly supervised framework that learns the feature representations of various lesion areas from histopathology whole slide images. The proposed framework consists of a contrastive learning network as the backbone and a designed contrastive dynamic clustering (CDC) module to embedding the lesion information into the feature representations. The proposed method was evaluated on a large scale endometrial whole slide image dataset. The experimental results have demonstrated that our method can learn discriminative feature representations for histopathology image classification and the quantitative performance of our method is close to the fully-supervision learning methods. The code is available at <a href="https://github.com/junl21/cdc">https://github.com/junl21/cdc</a>.

Introduction: Artifacts are often introduced during tissue collection and processing, slide preparation, and/or when generating whole slide images (WSI). The presence of artifacts has a negative impact on the digital pathology workflow as artifacts may hinder diagnostic reporting and can lead to false positive and false negative results when using image analysis algorithms or computer-aided diagnosis systems. Manual quality control of WSI is a time-consuming procedure and therefore automated quality control tools, which report and exclude artifacts, are highly desirable to streamline digital pathology workflows. To automate the quality control step, we developed SlideQC, an AI-based quality control tool that automatically detects, reports, and outlines artifacts such as air bubbles, dust/debris, folds, out-of-focus,and pen marks, in both research and clinical workflows. Methods: SlideQC was trained with a DenseNet-based network using 1984 annotations for artifacts including air bubbles, dust/debris, folds, out-of-focus, and pen markers, across 254 Haematoxylin and Eosin (H&amp;E) stained WSI from more than 9 tissue types. A set of 2048 annotations from synthetically generated out-of-focus images was added to supplement the training data. The performance of the SlideQC was evaluated on an external test cohort of 49 WSI H&amp;E images sourced from the open-source database ‘HistoQCRepo’, across 375 annotations (tissue and artifact), and compared with the performance of HistoQC, an open-source quality control tool for digital pathology slides. Results: On the external test cohort, SlideQC showed high sensitivity, specificity, and F1-score with average values of 0.93, 0.99, and 0.93, across the five artifact types. In the same cohort, HistoQC attained an average sensitivity, specificity, and F1-score of 0.65, 0.79, and 0.54, respectively. Conclusions: SlideQC achieved high sensitivity, specificity, and F1-score on an external test cohort. SlideQC can add efficiency gains to a workflow by performing quality control on 100% of slides rather than the currently manually performed on only a subset of the slides in clinical pathology departments. SlideQC can allowthe triaging and alerting of slides containing a high level of artifact within a digital pathology workflow. The tool can also be used to exclude the artifact region from downstream analysis by subsequent image analysis algorithms. Citation Format: Daniela Rodrigues, Stefan Reinhard, Therese Waldburger, Daniel Martin, Suzana Couto, Inti Zlobec, Peter Caie, Erik Burlingame. SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5442.

Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

Around 1% of the population of the UK and North America have a diagnosis of coeliac disease (CD), due to a damaging immune response to the small intestine. Assessing whether a patient has CD relies primarily on the examination of a duodenal biopsy, an unavoidably subjective process with poor inter-observer concordance. Wei et al. [11] developed a neural network-based method for diagnosing CD using a dataset of duodenal biopsy whole slide images (WSIs). As all training and validation data came from one source, there was no guarantee that their results would generalize to WSIs obtained from different scanners and laboratories. In this study, the effects of applying stain normalization and jittering to the training data were compared. We trained a deep neural network on 331 WSIs obtained with a Ventana scanner (WSIs; CD: n=190; normal: n=141) to classify presence of CD. In order to test the effects of stain processing when validating on WSIs scanned on varying scanners and from varying laboratories, the neural network was validated on 4 datasets: WSIs of slides scanned on a Ventana scanner (WSIs; CD: n=48; normal: n=35), WSIs of the same slides rescanned on a Hamamatsu scanner (WSIs; CD: n=48; normal: n=35), WSIs of the same slides rescanned on an Aperio scanner (WSIs; CD: n=48; normal: n=35), and WSIs of different slides scanned on an Aperio scanner (WSIs; CD: n=38; normal: n=37).Without stain processing, the F1 scores of the neural network were 0.947, 0.619, 0.746, and 0.727 when validating on the Ventana validation WSIs, Hamamatsu and Aperio rescans of the Ventana validation WSIs, and Aperio WSIs from a different source respectively. With stain normalization, the performance of the neural network improved significantly with respective F1 scores 0.982, 0.943, 0.903, and 0.847. Stain jittering resulted in a better performance than stain normalization when validating on data from the same source F1 score 1.000, but resulted in poorer performance than stain normalization when validating on WSIs from different scanners (F1 scores 0.939, 0.814, and 0.747). This study shows the importance of stain processing, in particular stain normalization, when training machine learning models on duodenal biopsy WSIs to ensure generalizability between different scanners and laboratories.

SummaryObjectiveCervical cancer remains a major global health burden, where early detection is critical. Cytological and histological assessments aim to identify precancerous squamous intraepithelial lesions (SILs). While artificial intelligence and machine learning have shown promise, most approaches rely on cytology or are not tailored for SIL classification. The aim of this study is to develop and evaluate a weakly supervised, pixel-level machine learning framework for the histological classification of low grade and high grade SIL in whole slide images (WSIs). Specifically, we sought to assess whether an open source segmentation pipeline trained on sparsely annotated WSIs could accurately support slide-level diagnostic interpretation while minimizing annotation burden and maintaining clinical interpretability.MethodsWe propose a weakly supervised machine learning framework for classifying low grade and high grade SILs in whole-slide histological images. Using Random Forest classifiers for pixel-level segmentation, the system mimics pathologists by quantifying tissue components. Training required only sparse annotations from a limited set of WSIs, yielding millions of pixel-level samples and reducing annotation burden.ResultsApplied on a test set of 309 cervical WSIs, the system achieved over 96% concordance with expert pathologists, correctly distinguishing low grade LSIL, high grade HSIL, and normal epithelium, with only one false negative and a 7-10 false positives, depending on the used model.ConclusionsOur approach offers accurate, interpretable, and low-cost diagnostic support, with potential for integration into routine workflows, especially in resource-limited settings.

Working toward consensus among professionals in the identification of classical cervical cytomorphological characteristics in whole slide images

5578 Background: In the field of ovarian cancer diagnosis, predicting Homologous Recombination Deficiency (HRD) holds paramount importance for personalized treatment strategies. Histopathology tissue analysis is considered as the gold standard in cancer diagnosis, prognosis, and theranostic. Whole Slide Imaging, i.e the scanning and digitization of entire histology slide, is now being adopted in numerous pathology labs, allowing development of deep-learning analysis of the large amount of morphological features contained in these WSI. The aim of this study is the development of a cutting-edge deep learning model designed to predict HRD status directly from WSIs of ovarian cancer tissue samples. Methods: We introduce a DNN designed to predict the HRD status for patients with ovarian cancer from whole slide images (WSI), using a fusion-like model using both cellular information and tissue-level morphological features. The algorithm has been trained and evaluated using a cross-testing and cross-validation technique on 151 patients with at least one WSI from a discovery dataset using a 5-fold stratified framework. We then externally validated the model onto The Cancer Genome Atlas (TCGA) from which the HRD status is available (n=93). Results: The performance of HRD status prediction was assessed on the basis of area under curve (AUC).The proposed architecture achieved an AUC of 0.74 on the discovery cohort and 0.67 on the TCGA. Results to the pathologist could be provided immediately and integrated into the pathologist report. External validation on the PAOLA cohort dataset is ongoing. Conclusions: By harnessing the power of deep neural networks (DNN), we provide a rapid and scalable solution for HRD prediction, circumventing the limitations of traditional molecular assays. Successful integration of this deep learning model into routine pathology workflows could significantly enhance diagnostic efficiency, reduce the turnaround time and financial cost compared with molecular assay. It could finally inform clinicians in tailoring targeted therapeutic interventions for ovarian cancer patients, thereby advancing precision medicine in the context of HRD-associated ovarian cancers.

Whole Slide Image (WSI) classification often relies on weakly supervised Multiple Instance Learning (MIL) methods to handle gigapixel-resolution images. In various MIL methods, attention-based approaches have shown great potential in modern medicine for cancer diagnosis and treatment. These approaches can model the interrelationships among instances to achieve enhanced bag representation using instance scores and thus promote bag-level classification performance. However, the existing attention-based MIL methods face two challenges: (1) The attention-based instance scores cannot accurately represent the contribution of instances to bag-level classification, making it difficult to identify the discriminative regions in WSIs. (2) Whole-slide pathological image analysis frequently suffers from model overfitting and insufficient representation of positive samples for training. To address the problem of poor discriminative regions in WSIs, we design a module to acquire the accurate contribution weights of instances by introducing the Class Activation Map suitable for WSI (WSICAM). For the second challenge, we adopt a Cross-Slide Augmentation (CSA) module to construct new samples with mixed labels on the basis of discriminative instances for model training. Our framework is composed of two WSICAM modules and one CSA module. The experimental results and visualizations demonstrate that our method achieves state-of-the-art in WSI classification on widely used datasets and exhibits robust capabilities in tumor lesion localization.

Mitotic rate counting is essential in pathologic evaluations in melanoma. The American Joint Committee on Cancer recommends reporting the number of mitotic figures (MFs) in a 1-mm2 area encompassing the "hot spot." There is currently no standard procedure for delineating a 1-mm2 region of interest for MF counting on a digital whole slide image (WSI) of melanoma. To establish a standardized method to enclose a 1-mm2 region of interest for MF counting in melanoma based on WSIs and assess the method's effectiveness. Whole slide images were visualized using the ImageScope viewer (Aperio). Different monitors and viewing magnifications were explored and the annotation tools provided by ImageScope were evaluated. For validation, we compared mitotic rates obtained from WSIs with our method and those from glass slides with traditional microscopy with 30 melanoma cases. Of the monitors we examined, a 32-inch monitor with 3840 × 2160 resolution was optimal for counting MFs within a 1-mm2 region of interest in melanoma. When WSIs were viewed in the ImageScope viewer, ×10 to ×20 magnification during screening could efficiently locate a hot spot and ×20 to ×40 magnification during counting could accurately identify MFs. Fixed-shape annotations with 500 × 500-μm squares or circles can precisely and efficiently enclose a 1-mm2 region of interest. Our method on WSIs was able to produce a higher mitotic rate than with glass slides. Whole slide images may be used to efficiently count MFs. We recommend fixed-shape annotation with 500 × 500-μm squares or circles for routine practice in counting MFs for melanoma.

1 Abstract In pathology, reconstructing adjacent tissue parts enables an overview of the macro environment of objects like tumors. Especially, malignoma are of interest to verify invasion and resection margins, as patients with positive margins face a higher mortality risk. Reassembling image fragments is widely used in other domains, but adjacent blocks in pathology are mostly analyzed separately missing global context. In this project, neighboring tissue of pig organ whole slide images (WSI) are reconstructed without a ground truth based on histological sections at the end of a complex work-up process. Histological tissue slices with artifacts, frayed or disrupted boundaries and sometimes missing pieces complicate the puzzling task. Thus, typical approaches such as direct feature comparison of tissue boundaries or estimating a tiles position based on an overview image or a known structures are not applicable. A new approach is presented using partial image registration where only parts of a fixed and a moving image are aligned for adjacency. In contrast to existing projects aligning subsequent tissue slices of the same block, WSIs from separated blocks will be reassembled for adjacency. The used three stage vision transformer extracts image features on various scales, compares neighboring tiles by shape, color and texture and predicts transformation parameters. Even though the pipeline is capable of handling rigid transformation such as rotation or reflection, only translation is currently supported due to the limited training set. Supervised training of the network can be realized using a puzzle generator creating irregular shaped fragments of masked whole slide images. The factorized trained neural network is embedded into a sophisticated histopathological vision transformer alignment (HiViTAlign) pipeline executing the following steps in roughly 10 seconds per reassembled tissue puzzle: First, extract the specimen and mask the background in each whole slide image. Second, compare tile boundaries using partial image registration. Third, calculate the adjacency by boundary proximity for each image pair. Fourth, determine a minimal spanning tree to optimize adjacency of pairwise registrations and transformations for tissue reconstruction. The python source code for HiViTAlign to start puzzling with WSIs or other objects is available at https://github.com/cpheidelberg/HiViTAlign. The generator for creating a dataset with irregular shaped tiles can be downloaded from https://github.com/cpheidelberg/ImagePuzzleGenerator. 2 Author summary Histopathology as the microscopic analysis of tissue remains the gold standard for evaluating tumors, especially when assessing resection margins. However, the physical processing of tissue disrupts its original three dimensional structure, leaving pathologists with fragmented, two-dimensional slices that lack spatial context. This fragmentation makes it difficult to understand the full extent and orientation of tumors and to correlate pathology results with radiological imaging used in surgical planning. In this study, we present a computational pipeline for histopathological vision transformer alignment (HiViTAlign) that reassembles fragmented histological tissue sections, similar to solving a jigsaw puzzle. Using a deep learning model based on Vision Transformers, our method predicts how individual tissue fragments are spatially related and outputs transformation parameters for adjacency. While the pipeline is designed to accommodate a variety of rigid transformations (e.g., rotation and scaling), its current implementation, constrained by the limited diversity of the training dataset, focuses solely on predicting translational shifts between fragments. A custom dataset generator was developed to create realistic puzzles from whole slide images, assigning original coordinates to each fragment to enable supervised training. The full pipeline was evaluated on both synthetic datasets and real-world whole slide images, demonstrating its ability to reconstruct tissue cross-sections without requiring a reference image. This method may support more accurate spatial interpretation of pathological specimens and better integration with surgical imaging data. The open-source Python code, we developed, invites collaboration and innovation, reflecting our commitment to advancing computational pathology through technology and shared resources. Paper to be submitted to PLOS Computational Biology .

BackgroundIn-person pathologist trainings during the COVID-19 pandemic became impossible, necessitating a shift to remote-digital whole slide image (WSI) training. High concordance between WSI and glass slide scores from the same...

Invasive ductal carcinoma (IDC) is the most common type of breast cancer. Every year a numerous number of women in this world are diagnosed as having IDC. Accurately detecting IDC is a time consuming and challenging task as the pathologists need to focus on the specific regions of whole slide images (WSI) that contain IDC. Precise and early diagnosis of IDC is a must because it helps to estimate the subsequent tumor aggressiveness that can be caused by this type of breast cancer. The goal of this research is to create an automated system that will analyze the whole mount slide images of breast cancer specimens to indicate the exact positions of IDC inside of the slides and give a decision based on the results. A multilayered convolutional neural network is designed which is trained over a large number of whole slide images. The dataset consists of 162 cases of patients diagnosed with IDC. We found an accuracy of 89.34% in f1 score using convolutional neural network to achieve the state of the art result on IDC classification.