> 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/appendix/github-repositories.md).

# GitHub Repositories

{% embed url="<https://github.com/sbalci>" %}

{% embed url="<https://github.com/sbalci/ParaPathology>" %}

{% embed url="<https://github.com/sbalci/JournalWatchPBPath>" %}

{% embed url="<https://github.com/sbalci/AutoJournalWatch>" %}

{% embed url="<https://github.com/sbalci/histopathology-template>" %}

{% embed url="<https://github.com/sbalci/MyRCodesForDataAnalysis>" %}

{% embed url="<https://github.com/sbalci/serdarbalci>" %}

{% embed url="<https://github.com/sbalci/histopathR>" %}

{% embed url="<https://github.com/sbalci/pubmed>" %}

{% embed url="<https://github.com/sbalci/DeutschLernen>" %}

{% embed url="<https://github.com/sbalci/Kotu-Veri-Kilavuzu>" %}

{% embed url="<https://github.com/sbalci/jsurvival>" %}

* [sbalci/jsurvival](https://github.com/sbalci/jsurvival) — Comprehensive survival analysis module for jamovi and R with Kaplan-Meier, Cox regression, continuous cutpoint discovery, stage migration, and competing risks. See [jsurvival](/statistics-and-bioinformatics/jamovi/jsurvival.md).

{% embed url="<https://github.com/sbalci/ClinicoPathDescriptives>" %}

* [sbalci/ClinicoPathDescriptives](https://github.com/sbalci/ClinicoPathDescriptives) — Descriptive analysis, Table 1, data quality validation, and visualization for clinicopathological research. See [ClinicoPathDescriptives](/statistics-and-bioinformatics/jamovi/clinicopath-descriptives.md).

{% embed url="<https://github.com/sbalci/jjstatsplot>" %}

* [sbalci/jjstatsplot](https://github.com/sbalci/jjstatsplot) — Statistical visualization wrapper for ggstatsplot in jamovi with 18 analysis types. See [jjstatsplot](/statistics-and-bioinformatics/jamovi/jjstatsplot.md).

{% embed url="<https://github.com/sbalci/meddecide>" %}

* [sbalci/meddecide](https://github.com/sbalci/meddecide) — Medical decision analysis, diagnostic test accuracy, ROC cutpoint optimization, and reliability toolkit for jamovi and R. See [meddecide](/statistics-and-bioinformatics/jamovi/meddecide.md).

{% embed url="<https://github.com/bibliometrics>" %}

{% embed url="<https://github.com/biostatistical>" %}

{% embed url="<https://github.com/dataeducation>" %}

{% embed url="<https://github.com/genomicanalysis>" %}

{% embed url="<https://github.com/histopathology>" %}

{% embed url="<https://github.com/journalwatch>" %}

{% embed url="<https://github.com/knowledgeextraction>" %}

{% embed url="<https://github.com/seeranalysis>" %}

{% embed url="<https://github.com/statisticial-modelling-center>" %}

## Other Repositories

{% embed url="<https://github.com/broadinstitute/celldega>" %}

{% embed url="<https://github.com/KAUST-Academy/Artificial-Intelligence-Courses>" %}

{% embed url="<https://github.com/clemsgrs/croma>" %}

{% embed url="<https://github.com/rmcelreath/stat_rethinking_2026>" %}

* [rmcelreath/stat\_rethinking\_2026](https://github.com/rmcelreath/stat_rethinking_2026) — Statistical Rethinking (2026 Edition) repository by Richard McElreath. See [Statistical Rethinking (2026 Edition)](/appendix/clippings/statistical-rethinking-2026-edition.md).

{% embed url="<https://github.com/cytomine/cytomine>" %}

* [cytomine/cytomine](https://github.com/cytomine/cytomine) — Open-source web platform for collaborative analysis and multi-gigapixel whole-slide image management. See [Cytomine](/computational-digital-and-mathematical-pathology/cytomine.md).

{% embed url="<https://github.com/TissueImageAnalytics/cytomine-app>" %}

* [TissueImageAnalytics/cytomine-app](https://github.com/TissueImageAnalytics/cytomine-app) — TIAToolbox model implementations (HoVer-Net, KongNet, NuClick) packaged as Dockerized Cytomine apps by the TIA Centre, University of Warwick. See [Cytomine](/computational-digital-and-mathematical-pathology/cytomine.md).

{% embed url="<https://github.com/digitalpathologybern/hover_next_train>" %}

* [digitalpathologybern/hover\_next\_train](https://github.com/digitalpathologybern/hover_next_train) — Training and evaluation code for HoVer-NeXt (ConvNeXt-V2 based nuclear instance segmentation and classification). See [HoVer-NeXt](/computational-digital-and-mathematical-pathology/hover-next.md).

{% embed url="<https://github.com/digitalpathologybern/hover_next_inference>" %}

* [digitalpathologybern/hover\_next\_inference](https://github.com/digitalpathologybern/hover_next_inference) — Multi-threaded whole-slide image inference pipeline and QuPath export for HoVer-NeXt. See [HoVer-NeXt](/computational-digital-and-mathematical-pathology/hover-next.md).

{% embed url="<https://github.com/saramcardle/FS2K>" %}

* [saramcardle/FS2K](https://github.com/saramcardle/FS2K) — From Samples to Knowledge: QuPath training course materials, step-by-step Jupyter notebooks, and workflows by Sara McArdle and Zbigniew Mikulski (La Jolla Institute for Immunology). See [From Samples to Knowledge 2025: QuPath Training Course](/appendix/clippings/from-samples-to-knowledge-2025-qupath-training-course.md).

{% embed url="<https://github.com/BIOP/qupath-extension-warpy>" %}

* [BIOP/qupath-extension-warpy](https://github.com/BIOP/qupath-extension-warpy) — QuPath extension for multi-modal, non-rigid whole-slide image registration using elastix and BigWarp.

{% embed url="<https://github.com/cobanov/awesome-fly>" %}

* [cobanov/awesome-fly](https://github.com/cobanov/awesome-fly) — A curated collection of fruit fly (*Drosophila melanogaster*) connectome projects, covering MaleCNS, FlyWire, whole-brain simulations, embodied models, games, and graph analysis toolkits by Mert Cobanov. See \[\[Awesome Fly: Curated Fruit Fly Connectome Projects]].

{% embed url="<https://github.com/cobanov/fly-connectome-template>" %}

* [cobanov/fly-connectome-template](https://github.com/cobanov/fly-connectome-template) — Starter template for fruit fly connectome experiments combining the MaleCNS soma atlas, Flybody mesh, and a React + Three.js workbench.

{% embed url="<https://github.com/GPEC/Multiplex-immunofluorescence-image-analysis-with-QuPath>" %}

* [GPEC/Multiplex-immunofluorescence-image-analysis-with-QuPath](https://github.com/GPEC/Multiplex-immunofluorescence-image-analysis-with-QuPath) — Workshop files and exercises for Sebastian Gilbert’s open mIF image analysis course in QuPath (UBC MAPcore): installing QuPath, digital image concepts, tissue and cell segmentation, cell classification, and quantitative/spatial analysis. Slides on Zenodo (CC BY 4.0, doi:10.5281/zenodo.22084859). See \[\[Multiplex Immunofluorescence Image Analysis with QuPath — Part 1: Understanding Digital Images]].

{% embed url="<https://github.com/DIDSR/HistoGen>" %}

* [DIDSR/HistoGen](https://github.com/DIDSR/HistoGen) — Open-source computational pathology toolbox and conditional diffusion model (DDPM) developed by the FDA Center for Devices and Radiological Health (CDRH/DIDSR, Regulatory Science Tool RST26DP02.01) to generate synthetic cell nuclei and H\&E histopathology images from segmentation masks. Checkpoints hosted on Hugging Face ([didsr/HistoGen](https://huggingface.co/didsr/HistoGen)). See \[\[HistoGen: Histopathology Cell Nuclei Image Generation Tool]] and \[\[Regulatory Science Tools Catalog: Digital Pathology (FDA CDRH)]].

{% embed url="<https://github.com/DIDSR/HTT>" %}

* [DIDSR/HTT](https://github.com/DIDSR/HTT) — R package and validation dataset from the FDA CDRH/DIDSR High-Throughput Truthing (HTT) project (Regulatory Science Tool RST26DP01.01). Contains 7,898 stromal tumor-infiltrating lymphocyte (sTILs) density annotations across 640 ROIs from 64 breast cancer WSIs with statistical utility functions for multi-reader multi-case (MRMC) agreement modeling. See \[\[Regulatory Science Tools Catalog: Digital Pathology (FDA CDRH)]].

{% embed url="<https://github.com/DIDSR/DxGoals>" %}

* [DIDSR/DxGoals](https://github.com/DIDSR/DxGoals) — R-Shiny software application developed by FDA CDRH/DIDSR (Regulatory Science Tool RST24MD19.01) for determining, visualizing, and analyzing clinically meaningful performance goals (sensitivity, specificity, positive/negative likelihood ratios) for diagnostic tests based on risk stratification thresholds. See \[\[Regulatory Science Tools Catalog: Digital Pathology (FDA CDRH)]].

{% embed url="<https://github.com/DIDSR/SegVal-WSI>" %}

* [DIDSR/SegVal-WSI](https://github.com/DIDSR/SegVal-WSI) — Python performance evaluation tool developed by FDA CDRH/DIDSR (Regulatory Science Tool RST24MD06.02) for digital pathology whole-slide image segmentation algorithms, computing pooled/macro Dice scores and bootstrapped confidence intervals across multi-ROI datasets. See \[\[Regulatory Science Tools Catalog: Digital Pathology (FDA CDRH)]].

{% embed url="<https://github.com/didsr/ValidPath>" %}

* [didsr/ValidPath](https://github.com/didsr/ValidPath) — End-to-end WSI processing and ML performance assessment toolkit developed by FDA CDRH/DIDSR (Regulatory Science Tool RST24CV11.01). Features standardized image patch extraction, back-mapping of detected ROIs into Aperio ImageScope-compatible XML annotations for pathologist review, and ROC/AUC performance evaluation with confidence intervals. See \[\[Regulatory Science Tools Catalog: Digital Pathology (FDA CDRH)]].

{% embed url="<https://github.com/zhihuanglab/CytoFormer>" %}

* [zhihuanglab/CytoFormer](https://github.com/zhihuanglab/CytoFormer) — Official inference codebase and models for CytoFormer, a molecularly supervised cell foundation model for single-cell histopathology classification across 16 organs and 23 cell types (UPenn / Yao, Li, Yu & Huang, Precision Pathology 2026). Pretrained weights available on Hugging Face ([zhihuanglab/CytoFormer](https://huggingface.co/zhihuanglab/CytoFormer)); interactive WSI browser at [zhihuanglab.github.io/CytoFormer](https://zhihuanglab.github.io/CytoFormer/); active learning integration via [TissueLab](https://app.tissuelab.org/community). See \[\[CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification]].

{% embed url="<https://github.com/owkin/histoplus>" %}

* [owkin/histoplus](https://github.com/owkin/histoplus) — Cell detection, segmentation, and 13-class classification library integrating the distilled Bioptimus H0-mini pathology foundation model (86M params) within a CellViT architecture (Adjadj et al., Owkin / Bioptimus / MOSAIC, Journal of Pathology Informatics 2026). Pretrained 20× and 40× weights on Hugging Face ([Owkin-Bioptimus/histoplus](https://huggingface.co/Owkin-Bioptimus/histoplus)); supports WSI batch extraction, CLI, and QuPath-compatible GeoJSON export. See \[\[HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides]].

{% embed url="<https://github.com/cytario/cytario-web>" %}

* [cytario/cytario-web](https://github.com/cytario/cytario-web) — Open-core web-based Image Management System (IMS) and viewer for digital pathology and spatial biology. Built on React 19, Viv (Harvard Medical School HIDIVE Lab), deck.gl, DuckDB-WASM, and Apache Arrow to stream petabyte-scale OME-TIFF, OME-Zarr, and Parquet data directly from S3 storage with Keycloak multi-tenancy. See [Cytario](/computational-digital-and-mathematical-pathology/cytario.md).

{% embed url="<https://github.com/mostafajahanifar/nuclick_torch>" %}

* [mostafajahanifar/nuclick\_torch](https://github.com/mostafajahanifar/nuclick_torch) — PyTorch implementation of NuClick for interactive and point-prompted nuclear, cell, and gland instance segmentation (Jahanifar, Koohbanani, Tajeddin & Rajpoot, Medical Image Analysis 2020). Utilizes a 5-channel input tensor (RGB + target inclusion map + neighbour exclusion map) with dedicated NuClick CNN and U-Net architectures (Dice 0.874+). Deployed as the foundational annotation expansion engine in Owkin's \[\[HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides]] to standardize 108k training nuclei and derive Hungarian consensus across 212k multi-pathologist reviews. See \[\[NuClick]].