Analysis and Validation of Image Search Engines in Histopathology.

Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient tissue comparison for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient tissue comparison. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets (1269 patients) and three public datasets (1207 patients), totaling more than 200, 000 patches from 38 different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.

On image search in histopathology.

Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.

A self-supervised framework for cross-modal search in histopathology archives using scale harmonization.

The exponential growth of data across various medical domains has generated a substantial demand for techniques to analyze multimodal big data. This demand is particularly pronounced in fields such as computational pathology due to the diverse nature of the tissue. Cross-modal retrieval aims to identify a common latent space where different modalities, such as image-text pairs, exhibit close alignment. The primary challenge, however, often lies in the representation of tissue features. While language models can be trained relatively easily, visual models frequently struggle due to the scarcity of labeled data. To address this issue, the innovative concept of harmonization has been introduced, extending the learning scheme distillation without supervision, known as DINO. The harmonization of scale refines the DINO paradigm through a novel patching approach, overcoming the complexities posed by gigapixel whole slide images in digital pathology. Experiments conducted on diverse datasets have demonstrated that the proposed approach significantly enhances cross-modal retrieval in tissue imaging. Moreover, it exhibits vast potential for other fields that rely on gigapixel imaging.

Creating an atlas of normal tissue for pruning WSI patching through anomaly detection.

Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.

Immunohistochemistry Biomarkers-Guided Image Search for Histopathology.

Medical practitioners use a number of diagnostic tests to make a reliable diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diagnosis and tumor detection. However, recently a variety of immunohistochemistry (IHC) stained slides can be requested by pathologists to examine and confirm diagnoses for determining the subtype of a tumor when this is difficult using H&E slides only. Deep learning (DL) has received a lot of interest recently for image search engines to extract features from tissue regions, which may or may not be the target region for diagnosis. This approach generally fails to capture high-level patterns corresponding to the malignant or abnormal content of histopathology images. In this work, we are proposing a targeted image search approach, inspired by the pathologists' workflow, which may use information from multiple IHC biomarker images when available. These IHC images could be aligned, filtered, and merged together to generate a composite biomarker image (CBI) that could eventually be used to generate an attention map to guide the search engine for localized search. In our experiments, we observed that an IHC-guided image search engine can retrieve relevant data more accurately than a conventional (i.e., H&E-only) search engine without IHC guidance. Moreover, such engines are also able to accurately conclude the subtypes through majority votes.

Composite Biomarker Image for Advanced Visualization in Histopathology.

Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer diagnosis and subtyping. It requires cross-staining comparison among Whole Slide Images (WSIs) of IHCs and hematoxylin and eosin (H&E) slides. Currently, pathologists examine the visually co-localized areas across IHC and H&E glass slides for a final diagnosis, which is a tedious and challenging task. Moreover, visually inspecting different IHC slides back and forth to analyze local co-expressions is inherently subjective and prone to error, even when carried out by experienced pathologists. Relying on digital pathology, we propose "Composite Biomarker Image" (CBI) in this work. CBI is a single image that can be composed using different filtered IHC biomarker images for better visualization. We present a CBI image produced in two steps by the proposed solution for better visualization and hence more efficient clinical workflow. In the first step, IHC biomarker images are aligned with the H&E images using one coordinate system and orientation. In the second step, the positive or negative IHC regions from each biomarker image (based on the pathologists' recommendation) are filtered and combined into one image using a fuzzy inference system. For evaluation, the resulting CBI images, from the proposed system, were evaluated qualitatively by the expert pathologists. The CBI concept helps the pathologists to identify the suspected target tissues more easily, which could be further assessed by examining the actual WSIs at the same suspected regions.

Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images.

In dealing with the lack of sufficient annotated data and in contrast to supervised learning, unsupervised, self-supervised, and semi-supervised domain adaptation methods are promising approaches, enabling us to transfer knowledge from rich labeled source domains to different (but related) unlabeled target domains, reducing distribution discrepancy between the source and target domains. However, most existing domain adaptation methods do not consider the imbalanced nature of the real-world data, affecting their performance in practice. We propose to overcome this limitation by proposing a novel domain adaptation approach that includes two modifications to the existing models. Firstly, we leverage the focal loss function in response to class-imbalanced labeled data in the source domain. Secondly, we introduce a novel co-training approach to involve pseudo-labeled target data points in the training process. Experiments show that the proposed model can be effective in transferring knowledge from source to target domain. As an example, we use the classification of prostate cancer images into low-cancerous and high-cancerous regions.

Whole slide image representation in bone marrow cytology.

One of the goals of AI-based computational pathology is to generate compact representations of whole slide images (WSIs) that capture the essential information needed for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision-support tools in hematology. In this study, we leverage our previously published end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSIs, which enables the direct use of individual cells as inputs rather than WSI patches. We then construct bags of individual cell features from each WSI, and apply multiple instance learning to extract their vector representations. To evaluate the quality of our representations, we conducted WSI retrieval and classification tasks. Our results show that we achieved a mAP@10 of 0.58 ±0.02 in WSI-level image retrieval, surpassing the random-retrieval baseline of 0.39 ±0.1. Furthermore, we predicted five diagnostic labels for individual aspirate WSIs with a weighted-average F1 score of 0.57 ±0.03 using a k-nearest-neighbors (k-NN) model, outperforming guessing using empirical class prior probabilities (0.26 ±0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.

Cell projection plots: A novel visualization of bone marrow aspirate cytology.

Deep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce novel, automatically generated visual summaries of bone marrow aspirate specimens called cell projection plots (CPPs). Encompassing relevant biological patterns such as neutrophil maturation, CPPs provide a compact summary of bone marrow aspirate cytology. To gauge clinical relevance, CPPs were inspected by 3 hematopathologists, who decided whether corresponding diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with a matching degree of 85%. Our finding suggests CPPs can represent clinically relevant information from bone marrow aspirate specimens and may be used to efficiently summarize bone marrow cytology to pathologists. CPPs could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic-support tool for digital pathology workflows.

Learning binary and sparse permutation-invariant representations for fast and memory efficient whole slide image search.

Considering their gigapixel sizes, the representation of whole slide images (WSIs) for classification and retrieval systems is a non-trivial task. Patch processing and multi-Instance Learning (MIL) are common approaches to analyze WSIs. However, in end-to-end training, these methods require high GPU memory consumption due to the simultaneous processing of multiple sets of patches. Furthermore, compact WSI representations through binary and/or sparse representations are urgently needed for real-time image retrieval within large medical archives. To address these challenges, we propose a novel framework for learning compact WSI representations utilizing deep conditional generative modeling and the Fisher Vector Theory. The training of our method is instance-based, achieving better memory and computational efficiency during the training. To achieve efficient large-scale WSI search, we introduce new loss functions, namely gradient sparsity and gradient quantization losses, for learning sparse and binary permutation-invariant WSI representations called Conditioned Sparse Fisher Vector (C-Deep-SFV), and Conditioned Binary Fisher Vector (C-Deep-BFV). The learned WSI representations are validated on the largest public WSI archive, The Cancer Genomic Atlas (TCGA) and also Liver-Kidney-Stomach (LKS) dataset. For WSI search, the proposed method outperforms Yottixel and Gaussian Mixture Model (GMM)-based Fisher Vector both in terms of retrieval accuracy and speed. For WSI classification, we achieve competitive performance against state-of-art on lung cancer data from TCGA and the public benchmark LKS dataset.