Download Google Slides Image __EXCLUSIVE_

What file format (ODP, PPT, PPTX) is the presentation in? What format are the inserted graphics in? Export to HTML of ODP appears to creates an image of the entire slide here, regardless of what content the slide includes, although I have not tested this with every possible graphics format.

Images in the Slides API are a type of page element. As with any pageelement, you specify the visual size and position of the image using the sizeand transform properties of thePageElement.For more details on how to correctly size and position your image, seeSize & position shapes.

Download Google Slides Image

Download Zip 🔥 https://urlin.us/2y2EEF 🔥

Hi, there!

When I open HE slides with the latest version of QuPath, v0.3.1, I can only see a white image, but it is ok to open IHC slides, and v0.3.0 works well for both HE and IHC slides. Does anyone have the same problem?

My system is MAC OS 12.0.1. Thank you!

I think that what is happening is that Bio-Formats is wrongly identifying the macro/overview images as low-resolution pyramid levels, rather than separate images, which causes the misbehaviour when zoomed out.

QuPath v0.3.0 used Bio-Formats v6.7.0 whereas QuPath v0.3.1 uses Bio-Formats v6.8.0. The new Bio-Formats version worked fine for all images I tested, and I wanted to make the improvements available quicker than the time needed for the next major QuPath update, but unfortunately it seems to have a bug here.

Just catching up on this thread now after the holidays. It does indeed look as though this is an issue with the SVS macro and label image changes introduced in Bio-Formats 6.8.0. I have opened a GitHub Issue to track this issue and we will aim to have it resolved in a follow up release: SVS: Incorrect pyramid levels with 6.8.0 Issue #3757 ome/bioformats GitHub

While manual microscopic inspection of histopathology slides remains the gold standard for evaluating the malignancy, subtype, and treatment options for cancer1, pathologists and oncologists increasingly rely on molecular assays to guide personalization of cancer therapy2. These assays can be expensive and time-consuming3 and, unlike histopathology images, are not routinely collected, limiting their use in retrospective and exploratory research. Manual histological evaluation, on the other hand, presents several clinical challenges. Careful inspection requires significant time investment by board-certified anatomic pathologists and is often insufficient for prognostic prediction. Several evaluative tasks, including diagnostic classification, have also reported low inter-rater agreement across experts and low intra-rater agreement across multiple reads by the same expert4,5. Furthermore, manual assessment of the expression of specific genes from histopathology has not to our knowledge been demonstrated.

Modern computer vision methods present the potential for rapid, reproducible, and cost-effective clinical and molecular predictions. Over the past decade, the quantity and resolution of digitized histology slides has dramatically improved6. At the same time, the field of computer vision has made significant strides in pathology image analysis7,8, including automated prediction of tumor grade9, mutational subtypes10, and gene expression signatures across cancer types11,12,13. In addition to achieving diagnostic sensitivity and specificity metrics that match or exceed those of human pathologists14,15,16, automated computational pathology can also scale to service resource-constrained settings where few pathologists are available. As a result, there may be opportunities to integrate these technologies into the clinical workflows of developing countries17.

One emerging solution has been the automated computation of human-interpretable image features (HIFs) to predict clinical outcomes. HIF-based prediction models often mirror the pathology workflow of searching for distinctive, stage-defining features under a microscope and offer opportunities for pathologists to validate intermediate steps and identify failure points. In addition, HIF-based solutions enable incorporation of histological knowledge and expert pixel-level annotations, which increases predictive power. Studied HIFs span a wide range of visual features, including stromal morphological structures25, cell and nucleus morphologies26, shapes and sizes of tumor regions27, tissue textures28, and the spatial distributions of tumor-infiltrating lymphocytes (TILs)29,30.

In order to test our approach on a diverse array of histopathology images, we obtained 2917 hematoxylin and eosin (H&E)-stained, formalin-fixed, and paraffin-embedded (FFPE) WSIs from The Cancer Genome Atlas (TCGA), corresponding to 2634 distinct patients. These images, each scanned at either 20 or 40 magnification, represented patients with skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), breast cancer (BRCA), lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC) from 95 distinct clinical sites. These five cancer types were selected given their relevance to immuno-oncology therapies and their image availability in TCGA. We summarize the characteristics of TCGA patients in Supplementary Table 1. To supplement the TCGA analysis cohort, we obtained 4158 additional WSIs for the five cancer types to improve model robustness.

a Methodology for extracting human-interpretable image features (HIFs) from high-resolution, digitized images stained with hematoxylin and eosin (H&E). b Summary statistics on the number of whole-slide images (WSIs), distinct patients, and annotations curated from The Cancer Genome Atlas (TCGA) and additional datasets. c Unprocessed portions of stomach adenocarcinoma (STAD) H&E-stained slides alongside corresponding heatmap visualizations of cell- and tissue-type predictions. Slide regions are classified into tissue types: cancer tissue (red), cancer-associated stroma (orange), necrosis (black), or normal (transparent). Pixels in cancer tissue or cancer-associated stroma areas are classified into cell types: lymphocyte (green), plasma cell (lime), fibroblast (orange), macrophage (aqua), cancer cell (red), or background (transparent).

a Uniform Manifold Approximation and Projection (UMAP) visualization of five cancer types reduced from the 607-dimension space defined by human-interpretable image feature (HIF) values into two dimensions. Each point represents a patient sample colored by cancer type. b Clustered heatmap of median Z-scores (computed pan-cancer) across cancer types for 20 HIFs, each representing one HIF cluster (defined pan-cancer). Hierarchical clustering was performed using average linkage and Euclidean distance. Clusters are annotated with a representative HIF chosen based on interpretability and high variance across cancer types.

In recent years, fusion approaches that combine deep learning with feature engineering have gained traction68,69,70,71. Our study combines exhaustive deep learning-based cell- and tissue-type classifications to compute image features that are both biologically relevant and human interpretable. We demonstrate that computed HIFs can recapitulate sequencing-based cell quantifications, capture canonical immune signatures such as leukocyte infiltration and TGF- expression, and robustly predict five molecular phenotypes relevant to the efficacy of targeted cancer therapies. We also demonstrate the generalizability of our associations, as evidenced by similarly predictive HIF clusters across biopsy images derived from five different cancer types. Notably, we show that our HIF-based approach, which integrates the predictive power of deep learning with the interpretability of feature engineering, achieves comparable performance to that of black-box models.

Lastly, during both model development and evaluation, we sought to emphasize robustness to real-world variability75. In particular, we supplemented TCGA WSIs with additional diverse datasets during CNN training, integrated pathologist feedback into model iterations, and evaluated HIF-based model performance on hold-out sets composed exclusively of samples from unseen tissue source sites, improving upon prior approaches to predicting molecular outcomes from TCGA H&E images26,76.

Our study data from TCGA carries several limitations. First, biopsy images submitted to the TCGA dataset are biased toward primary tumors and tumors with more definitive diagnoses that may not generalize well to ordinary clinical settings. Indeed, associations identified in primary tumors may not necessarily generalize to metastatic settings (Supplementary Fig. 5). Second, TCGA is limited to images of H&E staining, which limits the breadth of information available to models. Integrating multimodal data containing stains against Ki-67 or immunohistological targets may increase confidence in cell classifications77. Third, batch effects in TCGA can originate from differing tissue collection, sectioning, and processing procedures. Our validation procedure of partitioning by tissue source site does not account for all possible data artifacts, but it does control for confounding by sample collection, extraction, and other site-specific variables. Our HIF-based approach also limits the impact of spurious associations introduced by batch effects by pre-defining features based on biological phenomena. Fourth, TCGA has limited treatment data and clinical endpoint data are less reliable than molecular data. As TCGA samples were made available in 201378, treatment regimens for these cases also predate the widespread adoption of immune checkpoint inhibitors. As such, our models were restricted to prediction of molecular phenotypes with relevance to drug response, in lieu of more direct clinical endpoints, such as RECIST79 and overall survival. While molecular phenotypes such as PD-L1 expression are informative for clinical endpoints such as sensitivity to immune checkpoint blockade80, the ability to robustly predict biomarkers does not necessarily translate into robust prediction of relevant endpoints. Ultimately, direct prediction of patient outcomes is needed for clinical integration. Our study provides an interpretable framework to generate hypotheses for clinically relevant biomarkers that can be validated in future prospective studies81. The curation of public datasets with matched pathology images and high-fidelity treatment information could help bridge the remaining gap. ff782bc1db