> For the complete documentation index, see [llms.txt](https://www.parapathology.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.parapathology.com/computational-digital-and-mathematical-pathology/digital-pathology-software.md). # Digital Pathology Software ## Choose by the job This is a research and learning shortlist, based on the tools' documented roles. Start by testing representative local files and a small pilot; the right choice depends on image formats, annotations, output, deployment, and support needs. | Task | Starting point | Check before adopting | | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | View, annotate, and quantify whole slides on a desktop | [QuPath](https://qupath.readthedocs.io/en/stable/docs/intro/about.html) | Actual scanner-file support, pixel calibration, extension compatibility, and export needs; QuPath's documentation says it is not intended for clinical, diagnostic, or therapeutic purposes | | Process scientific images or extracted slide regions with plugins and macros | [Fiji / ImageJ](https://imagej.net/software/fiji/) | Memory and image-size limits depend on the reader and plugins; test regions or downsampled images before attempting a full WSI | | Read image regions and pyramid levels from code | [OpenSlide](https://openslide.org/) | A reading library, not a complete annotation or analysis platform; check the supported format and representative files | | Manage a shared web-based slide collection with viewing and annotation | [Digital Slide Archive](https://digitalslidearchive.github.io/digital_slide_archive/) | Server deployment, access controls, storage, maintenance, and the HistomicsUI/HistomicsTK workflow | | Organise collaborative annotations and image-analysis projects in a browser | [Cytomine](https://doc.uliege.cytomine.org/) | An administered instance, project permissions, labels, and algorithm integration; see the [local tool note](/computational-digital-and-mathematical-pathology/cytomine.md) | For QuPath, begin with its [first steps](https://qupath.readthedocs.io/en/stable/docs/starting/first_steps.html) and [file-format guidance](https://qupath.readthedocs.io/en/stable/docs/intro/formats.html). Fiji's [image-size FAQ](https://imagej.net/learn/faq) and QuPath's [ImageJ exchange guide](https://qupath.readthedocs.io/en/stable/docs/advanced/imagej.html) explain why opening an extracted region can be more practical than loading a whole slide. For infrastructure work, use the [DSA deployment documentation](https://github.com/DigitalSlideArchive/digital_slide_archive/blob/master/devops/dsa/README.rst) or [Cytomine installation guide](https://doc.uliege.cytomine.org/admin-guide/docker/installation). ### A small adoption checklist * **Input:** Test your scanner, file variant, stain/channels, resolution levels, and calibration metadata. A supported extension does not guarantee every file variant works. * **Output:** Decide whether you need an image, annotation geometry, per-cell measurements, a slide-level table, or a model prediction. Check coordinate systems and units when moving results between tools. * **Reproducibility:** Record the application, extensions, model/checkpoint, settings, and export format; reopen the saved project and repeat a small analysis. * **Operations:** Identify who maintains a shared service, manages access and backups, and handles failures. Use approved storage and sharing routes for patient-derived images. * **License and intended use:** Check the exact release's code license, plus separate model-weight, dataset, and dependency terms. A code license does not establish diagnostic authorization or permission to redistribute images. These selection questions are editorial guidance, not a product certification or comparative benchmark. Continue with the [image-analysis pilot](/computational-digital-and-mathematical-pathology/image-analysis.md) for measurements, or the [usage and validation guide](/computational-digital-and-mathematical-pathology/about-the-usage-of-digital-pathology.md) for a diagnostic service. **Source check:** 1 October 2026, for this chooser and its official documentation. The specialist catalog below retains earlier research summaries; versions, performance claims, and availability need checking in their original context before adoption. ## Specialist tools and collected notes ### [Cytario](https://www.cytario.com) Open-core, cloud-native Image Management System (IMS) and web-based viewer for digital pathology and spatial biology. Built on React 19, [Viv](https://github.com/hms-dbmi/viv) (Harvard Medical School HIDIVE Lab), deck.gl, and DuckDB-WASM/Apache Arrow to stream petabyte-scale OME-TIFF, OME-Zarr, and Parquet data directly from S3-compatible storage with Keycloak multi-tenancy — see dedicated tool note: [Cytario](/computational-digital-and-mathematical-pathology/cytario.md). * **GitHub:** [cytario/cytario-web](https://github.com/cytario/cytario-web) — AGPL-3.0 * **Platform Portal:** [cytario.com](https://www.cytario.com) {% embed url="" %} {% embed url="" %} ### [Cytomine](https://uliege.cytomine.org/) Open-source, web-based platform for multi-gigapixel whole-slide image management, collaborative multi-observer annotation (PostGIS-backed spatial geometries), and distributed algorithmic analysis. See dedicated tool note: [Cytomine](/computational-digital-and-mathematical-pathology/cytomine.md). * **GitHub:** [cytomine/cytomine](https://github.com/cytomine/cytomine) * **Academic Portal:** [uliege.cytomine.org](https://uliege.cytomine.org/) * **Documentation:** [doc.uliege.cytomine.org](https://doc.uliege.cytomine.org/) * **TIA Centre Cytomine Apps:** [TissueImageAnalytics/cytomine-app](https://github.com/TissueImageAnalytics/cytomine-app) — Wraps TIAToolbox models (`cytomine-hovernet`, `cytomine-kongnet`, `cytomine-interactive-segmentation-nuclick`) into Cytomine Docker apps. {% embed url="" %} {% embed url="" %} {% embed url="" %} ### [NuClick](https://github.com/mostafajahanifar/nuclick_torch/) Interactive deep-learning framework for prompted segmentation of microscopic objects (nuclei, cells, and glands) from user point clicks (Jahanifar, Koohbanani, Tajeddin & Rajpoot, *Medical Image Analysis* 2020). Utilizes a 5-channel tensor (RGB + target inclusion map + neighbour exclusion map) with dedicated NuClick CNN and U-Net models (Dice 0.874+) to eliminate manual boundary drawing. Serves as the annotation and consensus foundation for Owkin's \[\[HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides]] — see dedicated tool note: [NuClick](/computational-digital-and-mathematical-pathology/digital-pathology-software/nuclick.md). * **PyTorch GitHub:** [mostafajahanifar/nuclick\_torch](https://github.com/mostafajahanifar/nuclick_torch/) — CC BY-NC-SA 4.0 * **Paper:** [DOI: 10.1016/j.media.2020.101771](https://doi.org/10.1016/j.media.2020.101771) * **Cytomine App:** Included in [TissueImageAnalytics/cytomine-app](https://github.com/TissueImageAnalytics/cytomine-app) (`cytomine-interactive-segmentation-nuclick`) {% embed url="" %} ### [CellQuant-Net](https://github.com/Falah-Jabar-Rahim/CellQuant-Net) End-to-end WSI nuclei quantification pipeline integrating deep-learning artifact quality assessment (WSI-QA), prior-guided nuclei detection and 3-class classification (**CellPrior-Net / CP-Net**), and spatial cell neighborhood network analysis (Jabar et al., *Journal of Pathology Informatics* 2026). Combines a UniRepLKNet-N large-kernel CNN with a 4-channel input (RGB + hematoxylin Difference of Gaussians prior) and GPU watershed post-processing, matching transformer accuracy while running 2x to 3x faster than CellViT. Exports directly to QuPath GeoJSON and computes tumor-infiltrating lymphocyte (TIL) prognostic density metrics — see dedicated tool note: [CellQuant-Net](/computational-digital-and-mathematical-pathology/digital-pathology-software/cellquant-net.md) and literature review \[\[CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H\&E Whole-Slide Images]]. * **GitHub:** [Falah-Jabar-Rahim/CellQuant-Net](https://github.com/Falah-Jabar-Rahim/CellQuant-Net) * **Paper:** [DOI: 10.1016/j.jpi.2026.100716](https://doi.org/10.1016/j.jpi.2026.100716); [arXiv:2607.00802](https://arxiv.org/abs/2607.00802) * **Video Tutorial:** [YouTube](https://youtu.be/RhCJnUfuYkA?is=Jc4keTUtecEcjeZd) {% embed url="" %} ### [RepLKNet](https://github.com/DingXiaoH/RepLKNet-pytorch) Foundational large-kernel convolutional architecture and PyTorch framework scaling depthwise convolutions up to 31×31 via structural re-parameterization and custom CUDA Implicit GEMM acceleration (Ding et al., *CVPR 2022*). By establishing that large kernels deliver an ultra-wide Effective Receptive Field (ERF) and high shape bias matching Vision Transformers, RepLKNet laid the theoretical and architectural foundation for next-generation, high-speed computational pathology encoders—including UniRepLKNet and the UniRepLKNet-N backbone powering \[\[CellQuant-Net]] / \[\[CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H\&E Whole-Slide Images]]—enabling whole-slide image analysis at 2x to 3x the speed of CellViT. See dedicated tool note: [RepLKNet](/computational-digital-and-mathematical-pathology/digital-pathology-software/replknet.md) and literature review \[\[Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs]]. * **PyTorch GitHub:** [DingXiaoH/RepLKNet-pytorch](https://github.com/DingXiaoH/RepLKNet-pytorch) — Apache-2.0 * **Paper:** [DOI: 10.1109/CVPR52688.2022.01167](https://doi.org/10.1109/CVPR52688.2022.01167); [arXiv:2203.06717](https://arxiv.org/abs/2203.06717) {% embed url="" %} ### ePMA.start – universal whole slide image viewer for digital pathology **An end-user viewer and tile server in one convenient package**\*\*\*\* [ORBIT IMAGE ANALYSIS](https://www.orbit.bio/) {% embed url="" %} ### [Micro-Manager](https://micro-manager.org/) Open-source microscope control and acquisition automation, integrated with ImageJ — see [Micro-Manager](https://github.com/sbalci/ParaPathology/tree/master/computational-digital-and-mathematical-pathology/micro-manager.md). {% embed url="" %} ### [OpenFlexure Microscope](https://openflexure.org/projects/microscope/) Open-source, 3D-printable robotic digital microscope featuring sub-100 nm positioning precision via monolithic compliant flexure mechanics, RMS DIN finite/infinity optics, Sangaboard motor control, Python REST API, automated whole-slide scanning, and validated applications in parasitology and telepathology — see dedicated tool note: [OpenFlexure Microscope](/computational-digital-and-mathematical-pathology/digital-pathology-software/openflexure-microscope.md) and literature review \[\[Robotic microscopy for everyone: the OpenFlexure Microscope]]. * **Website:** [openflexure.org/projects/microscope](https://openflexure.org/projects/microscope/) * **GitHub:** [rwb27/openflexure\_microscope](https://github.com/rwb27/openflexure_microscope) | **GitLab:** [openflexure/openflexure-microscope](https://gitlab.com/openflexure/openflexure-microscope) * **Paper:** Collins et al., *Biomedical Optics Express* 2020 ([DOI: 10.1364/BOE.385729](https://doi.org/10.1364/BOE.385729)) {% embed url="" %} {% embed url="" %} ### [M-Optimus](https://www.bioptimus.com/m-optimus) Multimodal biological world model and digital pathology foundation platform developed by Bioptimus (Paris, France). Connects routine Hematoxylin and Eosin (H\&E) whole-slide histology with molecular genetics (bulk RNA-seq and spatial transcriptomics) to perform in silico spatial transcriptomics—reconstructing spatial expression maps for 6,000+ genes directly from H\&E whole-slide images alone or paired with bulk RNA-seq. Validated against 10x Genomics Xenium Prime ($r = 0.81$) and deployed on archival clinical cohorts (TCIA) to identify spatial microenvironmental biomarkers of targeted therapy resistance — see dedicated tool note: [M-Optimus](/computational-digital-and-mathematical-pathology/digital-pathology-software/m-optimus.md) and literature review \[\[From Model to Patient: M-Optimus-1 for Spatial Biomarker Discovery in Ovarian Cancer]]. * **Website:** [bioptimus.com/m-optimus](https://www.bioptimus.com/m-optimus) * **Technical Launch:** [bioptimus.com/introducing-m-optimus](https://www.bioptimus.com/introducing-m-optimus) * **STELA Data Consortium:** [bioptimus.com/stela](https://www.bioptimus.com/stela) {% embed url="" %} ### [PathoActivationAtlas](https://github.com/KatherLab/PathoActivationAtlas) Open-source PyTorch and Captum-based interpretability framework developed by the Kather Lab (Else Kröner Fresenius Center for Digital Health, TU Dresden) to audit what morphological concepts are organized inside pathology foundation models (Gustav et al., *Cell Reports Medicine* 2026). Generates Fourier-parameterized class visualizations (CVs) and layer-wise activation atlases (AAs) with t-SNE grid aggregation, and includes an interactive desktop GUI viewer and blinded pathologist annotation software — see dedicated tool note: [PathoActivationAtlas](/computational-digital-and-mathematical-pathology/digital-pathology-software/pathoactivationatlas.md) and literature review \[\[Class visualizations and activation atlases for computational pathology]]. * **GitHub:** [KatherLab/PathoActivationAtlas](https://github.com/KatherLab/PathoActivationAtlas) — MIT License (Captum components under BSD-3-Clause) * **Paper:** Gustav et al., *Cell Reports Medicine* 2026 ([DOI: 10.1016/j.xcrm.2026.103054](https://doi.org/10.1016/j.xcrm.2026.103054)) {% embed url="" %} ### [TRIDENT](https://github.com/mahmoodlab/trident) Industrial-grade, open-source Python toolkit developed by the Mahmood Lab (Harvard Medical School / Brigham and Women's Hospital) for large-scale whole-slide image processing, tissue segmentation, and feature extraction across 33+ patch encoders (UNI, CONCH, Virchow, GigaPath, H-Optimus) and 8+ slide encoders (TITAN, PRISM, GigaPath, CHIEF). Features deep neural segmentation (HEST, GrandQC), multi-GPU sharding, QuPath GeoJSON contour exports, asynchronous SSD caching, crash-resilient deadlock recovery, and integration with the 42-task Patho-Bench suite — see dedicated tool note: [TRIDENT](/computational-digital-and-mathematical-pathology/digital-pathology-software/trident.md) and literature review \[\[Accelerating Data Processing and Benchmarking of AI Models for Pathology]]. * **GitHub:** [mahmoodlab/TRIDENT](https://github.com/mahmoodlab/trident) * **Documentation:** [trident-docs.readthedocs.io](https://trident-docs.readthedocs.io/en/latest/) * **Paper:** Zhang et al., arXiv 2025 ([DOI: 10.48550/arXiv.2502.06750](https://doi.org/10.48550/arXiv.2502.06750)) {% embed url="" %} ### [STAMP](https://github.com/KatherLab/STAMP) Solid Tumor Associative Modeling in Pathology, an end-to-end weakly supervised deep learning pipeline developed by the Kather Lab (Else Kröner Fresenius Center for Digital Health, TU Dresden; RWTH Aachen; University Medical Center Mainz) for clinical biomarker prediction, multi-target classification, continuous regression, and survival analysis directly from whole-slide images. Features standardized preprocessing across 18+ foundation model extractors (UNI2, Virchow2, CONCH1.5, H-Optimus, CTransPath), patient-level virtual slide concatenation, slide contextualizers (TITAN, PRISM, GigaPath, COBRA2, EAGLE, MADELEINE, CHIEF), multiple MIL architectures (ViT, TransMIL, Barspoon), attention heatmaps, class maps, top/bottom predictive tile extraction, and native Model Context Protocol (FastMCP) server orchestration (`mcp/server.py`) paired with the [STAMP-Workbench](https://github.com/KatherLab/STAMP-Workbench) web UI — see dedicated tool note: [STAMP](/computational-digital-and-mathematical-pathology/digital-pathology-software/stamp.md) and published protocol review \[\[From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology]]. * **GitHub:** [KatherLab/STAMP](https://github.com/KatherLab/STAMP) — MIT License * **Workbench UI:** [KatherLab/STAMP-Workbench](https://github.com/KatherLab/STAMP-Workbench) * **Paper:** El Nahhas et al., *Nature Protocols* 20, 293–316 (2025) ([DOI: 10.1038/s41596-024-01047-2](https://doi.org/10.1038/s41596-024-01047-2), [PMID: 39285224](https://pubmed.ncbi.nlm.nih.gov/39285224/)) {% embed url="" %} ### [Celldega](https://github.com/broadinstitute/celldega) Source-available Python + JavaScript toolkit from the Broad Institute for interactive exploration of spatial-omics and single-cell data in notebooks and web browsers. See the dedicated [Celldega note](/computational-digital-and-mathematical-pathology/digital-pathology-software/celldega.md). **License checked 1 October 2026:** [Broad Institute Academic Software License](https://github.com/broadinstitute/celldega/blob/726b57cfeff1a0e70b06b91b079bd592b91daeca/LICENSE.txt), at upstream commit `726b57c`. The grant is for specified educational and academic-research uses by academic/nonprofit researchers; commercial entities are directed to Broad for licensing. Do not infer permissive reuse from the availability of the source code. **Paper:** Fernandez N, Ishar J, Wang H, Ben Saad A, Lipinski M, Farhi SL. *Celldega: Integrated Toolkit for Visualization and Analysis of Spatial Data.* bioRxiv preprint, posted 2026-08-21 (DOI: 10.64898/2026.08.13.744672). #### The Challenge in Spatial Pathology Spatial-transcriptomics and high-plex spatial proteomics pair high-dimensional molecular measurements with high-resolution microscopy to characterize cellular phenotypes, cell–cell communication, and microenvironmental tissue architecture. However, as spatial technologies have matured, two major computational bottlenecks have emerged: 1. **Visualization & Scale Limits:** Open-source visualization tools fail to scale past \~1 billion transcripts and millions of segmented cells, suffering severe memory bloat or interface lag. 2. **Ecosystem & Usability Barriers:** Commercial viewers are costly, closed-source, vendor-locked, and lack integration with custom data pipelines (such as Scanpy, Squidpy, and AnnData/SpatialData). #### Technical Architecture & Engineering Innovations Celldega introduces an end-to-end framework combining Python preprocessing, optimized columnar file formats, and high-performance WebGL/WebAssembly frontends: * **DegaFiles & Row Group Storage Mode:** * Traditional tile-based architectures generate 50,000+ individual files (transcripts, cell polygons, gene matrices, and WebP Deep Zoom image tiles), exceeding cloud host limits. * Celldega implements a consolidated **Apache Parquet / GeoParquet** format with an analytical formula-based index (`row_group_index = tile_x * num_tiles_y + tile_y`). * Uses **ParquetWASM** (Rust compiled to WebAssembly) and **Apache Arrow** in-memory structures to execute **HTTP Range Requests** in the browser, streaming only the exact byte ranges required for the visible viewport without downloading entire datasets. Reduces file counts from >50,000 to \~10 consolidated Parquet files. * **Hardware-Accelerated Rendering:** Leverages [deck.gl](https://deck.gl/) for GPU-accelerated rendering of multi-channel microscopy image pyramids, polygon segmentation masks, and single-molecule transcript locations. #### Specialized Visualization Modes Celldega provides an integrated suite of views designed for different spatial analysis workflows: * **`Landscape`:** 2D interactive multi-layer tissue canvas displaying pyramidal microscopy (H\&E, DAPI, multichannel fluorescence), cell segmentation boundaries, individual transcripts, and spatial neighborhood overlays, complete with real-time gene search and categorical summary histograms. * **`Yearbook`:** Synchronized grid of cell "portraits" (configurable rows/cols) centered on individual cells for morphological comparison and rapid phenotyping. Supports stateless browser-side querying (`front_end_query`) or programmatic selection from AnnData. * **`CellCloud` & `NeighborhoodCloud`:** 3D orbit-camera views rendering millions of cell centroids and alpha-shape neighborhood polygons across serial sections and thick tissues (with on-demand centroid streaming to maintain 60 FPS). * **`Clustergram` & `Composition`:** Hierarchically clustered heatmaps with interactive dendrogram cutting (built on Clustergrammer), bi-directionally linked to spatial views (`spatial_clustergram`) so selecting clusters or genes instantly highlights corresponding cells on the tissue section. * **`Enrich`:** Integrated real-time gene-set enrichment widget connecting directly to the [Enrichr](https://maayanlab.cloud/Enrichr/) API (e.g. `CellMarker_2024`, GO) to functionally annotate marker gene lists from selected tissue regions. #### Validated Platforms & Scale * **Supported Technologies:** 10x Genomics Xenium & Xenium Prime, NanoString CosMx SMI, 10x Visium HD, Vizgen MERSCOPE, and single-cell Chromium. * **Scale Benchmarks:** Demonstrated on datasets exceeding 1 billion transcripts, multi-slice serial alignment datasets, and a full 3D reconstruction of a developing whole mouse head comprising over **four million cells**. * **Cloud & Pipeline Integration:** Integrates with WDL workflows on Terra.bio (`stp_segmentation_wdl`) for reproducible automated preprocessing with Cellpose, InstanSeg, and Starfish. #### Relevance to Digital & Computational Pathology Celldega bridges the gap between raw spatial-omics data processing and diagnostic/translational slide evaluation: * Enables lightweight, zero-install, browser-based exploration and public web galleries without demanding dedicated GPU server infrastructure. * Empowers pathologists to inspect single-cell morphology, verify segmentation boundaries against histological ground truth, and interrogate local microenvironmental gradients (e.g. tumor-immune interfaces, islet neighborhoods, tertiary lymphoid structures) in a single unified workspace. {% embed url="" %} {% embed url="" %} {% embed url="" %} {% embed url="" %} ### [HistoMetPath](https://github.com/drehsangharib/HistoMetPath) **Not software to adopt** — a single-author MIL research framework whose models are 30-slide development artefacts (best AUROC 0.68, no external validation). Read instead for its evaluation-governance code: a held-out test set that can be opened exactly once, enforced by checksums, a config-load refusal of the test split, and an execution counter spent before the first pixel is read — see [HistoMetPath](https://github.com/sbalci/ParaPathology/tree/master/computational-digital-and-mathematical-pathology/histometpath.md). {% embed url="" %} ### [WSInfer](https://github.com/SBU-BMI/wsinfer) Applies an already-trained patch-classification model across a whole slide and returns a per-tile probability map — as a QuPath overlay, or as CSV and GeoJSON from the command line. Eight models (TCGA tumour classifiers, pan-cancer lymphocytes, PatchCamelyon lymph-node metastasis, NCT-CRC-HE-100K colorectal tissue) come from a Hugging Face zoo; your own TorchScript model plus a config works too. **Use the QuPath extension, not the Python package**: the runtime has had no commit since July 2024 and its last two bug fixes are unreleased, while the extension is maintained by the QuPath group and requires QuPath 0.6 — see [WSInfer](/computational-digital-and-mathematical-pathology/wsinfer.md). {% embed url="" %} {% embed url="" %} ### [nanopath](https://github.com/MedARC-AI/nanopath) A lean harness from MedARC for **pretraining tile-level pathology foundation models from scratch**, modelled on Karpathy's nanochat: one GPU, 1,000,000 TCGA tile presentations, a 1e18-FLOP cap, then a fixed 12-dataset probe suite (drawn from THUNDER, PathoBench and LEOPARD) covering tile classification, nuclear segmentation, slide-level progression, mutation, survival and robustness. The point is to test a training idea cheaply before spending real compute on it. Apache-2.0; 139 commits since April 2026, essentially one author (Paul Scotti). The 120 GB pre-tiled TCGA parquet set is on Hugging Face as `medarc/nanopath`, so no WSI handling is needed to start — but reproducing the tiling from raw SVS needs the full \~13 TB open-access TCGA slide set. Its real value here is the **frozen-baseline table**: UNI-2-h, H-optimus-0, Virchow, Prov-GigaPath, Midnight-12K, OpenMidnight, EXAONE-Path-2.5, GenBio-PathFM and three DINOv2 sizes all run through one identical probe suite. On that scale the best community model (0.6676, single GPU) lands above Virchow (0.6591) and GigaPath (0.6456) but below GenBio-PathFM (0.6917), UNI-2-h (0.6782) and H-optimus-0 (0.6763) — with the caveat the maintainers state themselves, that each backbone's native feature width (384-d to 4608-d) feeds the probe heads uncontrolled. The methodological detail worth copying is in `benchmarking/`: every probe carries a **randomised-weight null audit** — 20 seeds of an untrained DINOv2-small pushed through the same path — and the per-dataset notes say plainly which probes fail it. UCLA-Lung progression has a null of 0.692 ± 0.004, above every natural-image DINOv2 checkpoint and level with the current leaderboard leader, and the note calls it "a caution flag" and "not a clean representation-quality readout in isolation". LEOPARD BCR is similar (null 0.633 ± 0.012). Neither top-level README carries those caveats into the headline `mean_probe_score`, which averages all eight probe families equally — so the ranking absorbs columns the per-dataset notes flag as noise. There is a noise guard, though not that one: a new leader must beat the old by 0.006 and survive a maintainer rerun at a different rng seed. {% embed url="" %} {% embed url="" %} {% embed url="" %} ### [HoVer-NeXt](https://github.com/digitalpathologybern/hover_next_train) Modernized, high-throughput nuclear instance segmentation and classification pipeline from the University of Bern (Digital Pathology group / Prof. Inti Zlobec; Baumann et al., MIDL 2024). Re-engineers the classic HoVer-Net architecture using ConvNeXt-V2 backbones (Tiny/Base/Large), decoupled multi-head U-Net decoders (5-channel distance maps and multi-class cell typing), native multi-threaded whole-slide inference (OpenSlide, CZI, Zarr), and direct export to QuPath `.tsv` measurement tables. Pretrained checkpoints available on Zenodo for Lizard-Mitosis (7 classes) and PanNuke (5 classes across 19 tissues) — see [HoVer-NeXt](/computational-digital-and-mathematical-pathology/hover-next.md). * **Training Code:** [digitalpathologybern/hover\_next\_train](https://github.com/digitalpathologybern/hover_next_train) * **Inference Pipeline:** [digitalpathologybern/hover\_next\_inference](https://github.com/digitalpathologybern/hover_next_inference) * **Paper:** [MIDL 2024 (OpenReview)](https://openreview.net/pdf?id=3vmB43oqIO) {% embed url="" %} {% embed url="" %} ### [From Samples to Knowledge 2025: QuPath Training Course](https://www.youtube.com/playlist?list=PLlGXRBscPbCCA1yGCThNqdYKgTPOvjigp) Comprehensive hands-on curriculum from the La Jolla Institute for Immunology (Dr. Zbigniew Mikulski & Dr. Sara McArdle) targeting modern QuPath v0.6.0+ bioimage workflows. Covers 18-plex multiplex immunofluorescence (RareCyte Orion), native deep-learning cell segmentation via Deep Java Library (DJL / InstanSeg), Groovy scripting pipelines for batch cohort processing, complex multi-marker composite phenotyping, multimodal image registration (Warpy / `qupath-extension-warpy`), spatial proximity/density metrics, and interactive Python/Jupyter bridging (QuBylab / Paquo) — see dedicated course note: [From Samples to Knowledge 2025: QuPath Training Course](/clippings/from-samples-to-knowledge-2025-qupath-training-course.md). * **Course Book:** [saramcardle.github.io/FS2K](https://saramcardle.github.io/FS2K/README.html) * **Repository:** [saramcardle/FS2K](https://github.com/saramcardle/FS2K) * **Video Playlist:** [YouTube Playlist (14 videos)](https://www.youtube.com/playlist?list=PLlGXRBscPbCCA1yGCThNqdYKgTPOvjigp) * **Registration Tool:** [BIOP/qupath-extension-warpy](https://github.com/BIOP/qupath-extension-warpy) {% embed url="" %} {% embed url="" %} ### [HistoCAM](https://github.com/cooopermaira/HistoCAM_Nature_Communications) Ambient, real-time digitization and datafication platform for glass-slide microscopy developed by researchers across Tulane University and Kitware, Inc. (Maira et al., *Nature Communications* 2026). Couples a 31-megapixel high space-bandwidth camera with a C++ streaming engine (`pathcam`) to passively composite multi-resolution WSI pyramids (2X to 40X) during standard optical examination, record pathologist search trajectories and dwell times, and execute real-time TensorRT AI segmentation directly at the microscope — see dedicated tool note: [HistoCAM](/computational-digital-and-mathematical-pathology/digital-pathology-software/histocam.md) and literature review [Ambient, real-time digitization and datafication of glass slide microscopy towards AI-at-the-microscope](/clippings/ambient-real-time-digitization-and-datafication-of-glass-slide-microscopy-towards-ai-at-the-microsco.md). * **GitHub:** [cooopermaira/HistoCAM\_Nature\_Communications](https://github.com/cooopermaira/HistoCAM_Nature_Communications) * **Paper:** [Nature Communications (DOI: 10.1038/s41467-026-77887-1)](https://doi.org/10.1038/s41467-026-77887-1) * **Zenodo Benchmark Data:** [Zenodo Record 21970445](https://zenodo.org/records/21970445) {% embed url="" %} {% embed url="" %} ## In this section * [PathoActivationAtlas](/computational-digital-and-mathematical-pathology/digital-pathology-software/pathoactivationatlas.md) * [NuClick](/computational-digital-and-mathematical-pathology/digital-pathology-software/nuclick.md) * [CellQuant-Net](/computational-digital-and-mathematical-pathology/digital-pathology-software/cellquant-net.md) * [RepLKNet](/computational-digital-and-mathematical-pathology/digital-pathology-software/replknet.md) * [TRIDENT](/computational-digital-and-mathematical-pathology/digital-pathology-software/trident.md) * [OpenFlexure Microscope](/computational-digital-and-mathematical-pathology/digital-pathology-software/openflexure-microscope.md) * [STAMP](/computational-digital-and-mathematical-pathology/digital-pathology-software/stamp.md) * [M-Optimus](/computational-digital-and-mathematical-pathology/digital-pathology-software/m-optimus.md) * [Celldega](/computational-digital-and-mathematical-pathology/digital-pathology-software/celldega.md) * [HistoCAM](/computational-digital-and-mathematical-pathology/digital-pathology-software/histocam.md)