> 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/clippings/cellprior-net-prior-guided-nuclei-detection-and-classification-for-h-and-e-whole-slide-images.md). # CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H\&E Whole-Slide Images CellPrior-Net (CP-Net) introduces an efficient, prior-guided CNN architecture for dense nuclei detection and 3-class classification (tumor, immune, other) on H\&E gigapixel whole-slide images. By coupl ## Summary Accurate single-cell quantification in hematoxylin and eosin (H\&E) whole-slide images (WSIs) is foundational for deciphering the tumor microenvironment (TME) and calculating quantitative biomarkers such as tumor-infiltrating lymphocyte (TIL) density. However, existing computational pathology pipelines face three major clinical translation bottlenecks: 1. **The Representation Bottleneck:** Standard deep learning networks ingest standard 3-channel RGB patches, ignoring the biochemical reality that hematoxylin stains nucleic acids (DNA/RNA) specifically, while eosin stains non-specific cytoplasmic and extracellular proteins. 2. **The Compute & Latency Bottleneck:** Modern vision transformer pipelines (e.g., CellViT, CellDETR) and heavy multi-branch networks require massive compute and slow CPU-bound post-processing, taking 10 to 40+ minutes per gigapixel WSI. 3. **The Artifact Dilemma:** Clinical WSIs are rife with tissue folds, knife chatter, air bubbles, out-of-focus blur, and pathologist pen ink. Processing uncurated tiles generates massive false-positive cell counts. Published in the *Journal of Pathology Informatics* (August 2026, [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)), **CellPrior-Net (CP-Net)** and its parent clinical workflow **\[\[CellQuant-Net]]** resolve these challenges: * **Prior-Guided 4-Channel Input:** Separates the hematoxylin ($H$) stain via Macenko optical density deconvolution, applies a multi-scale Difference of Gaussians (DoG) filter to capture nuclear chromatin density, and feeds a 4-channel tensor ($\[R, G, B, \text{DoG}]$) into the network. * **UniRepLKNet-N Large-Kernel Encoder:** Replaces heavy transformer self-attention with large-kernel depthwise convolutions, delivering a vast receptive field at fractional computational overhead. * **GPU-Accelerated Watershed Post-Processing:** Combines GPU erosion, Euclidean distance transforms, and multithreaded watershed with magnification-optimized hyperparameters (calibrated for $20\times$ and $40\times$). * **End-to-End WSI Pipeline (\[\[CellQuant-Net]]):** Integrates automated deep-learning Quality Assessment (WSI-QA) to eliminate artifacts prior to inference, extracts nuclei types, builds spatial cell-cell connectivity graphs, and exports native QuPath GeoJSON files. * **Rigorous Cross-Dataset Benchmark (\~10.4M Nuclei across 8 Datasets):** Evaluated against 10 state-of-the-art pipelines (CellViT, CellViT++, Cerberus, HoVer-Net, NuLite, PointNu-Net, CellDetr, CellSAM, LKCell, TSFD-Net) across multiple organs, scanners, and magnifications. CP-Net establishes the optimal Pareto efficiency frontier, matching CellViT panoptic quality while cutting inference time by **$2\times$ to $3\times$**. *** ## Technical Architecture & Mathematical Formulation ```typescript ┌────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ CELLPRIOR-NET (CP-NET) PIPELINE │ └────────────────────────────────────────────────────────────────────────────────────────────────────────┘ [ Input H&E Tile (256 × 256 px) ] │ ┌─────────────┴────────────────────────┐ │ │ │ (3-channel RGB) ▼ │ ┌─────────────────────────────────┐ │ │ Macenko Stain Deconvolution │ │ │ OD Conversion & Eigenplane │ │ └───────────────┬─────────────────┘ │ │ │ ▼ │ ┌─────────────────────────────────┐ │ │ Isolated Hematoxylin (H) │ │ │ 99th Percentile Normalization │ │ └───────────────┬─────────────────┘ │ │ │ ▼ │ ┌─────────────────────────────────┐ │ │ Multi-Scale DoG Filter │ │ │ σ ∈ {2.5, 3.5, 4.5} (40×) │ │ │ σ ∈ {1.6, 2.4, 3.2} (20×) │ │ └───────────────┬─────────────────┘ │ │ │ ▼ │ ┌─────────────────────────────────┐ │ │ DoG Nuclear Prior Map │ │ └───────────────┬─────────────────┘ │ │ └──────────────────────┬───────────────┘ │ Concatenate [R, G, B, DoG] ▼ ┌─────────────────────────────────────────────────────────────────┐ │ 4-CHANNEL INPUT TENSOR [B, 4, H, W] │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ UniRepLKNet-N LARGE-KERNEL CNN BACKBONE │ │ • Stage 1: Downsampling & Large-Kernel Depthwise Conv │ │ • Stage 2: Hierarchical Spatial Feature Extraction │ │ • Stage 3: High-Dimensional Morphological Encoding │ │ • Stage 4: Global Context Integration │ └───────────────────────────────┬─────────────────────────────────┘ │ Multi-Scale Skip Connections ▼ ┌─────────────────────────────────────────────────────────────────┐ │ U-NET-LIKE PROGRESSIVE FEATURE DECODER │ │ Feature concatenation & progressive spatial upsampling │ └───────────────┬─────────────────────────────────┬───────────────┘ │ │ ▼ ▼ ┌───────────────────────────────┐ ┌───────────────────────────────┐ │ Binary Detection Head │ │ 3-Class Classification Head │ │ Nuclei vs Background │ │ Tumor vs Immune vs Other │ │ Loss: Dice + FocalTversky │ │ Loss: CE + Dice + FocalTvers │ └───────────────┬───────────────┘ └───────────────┬───────────────┘ │ │ └───────────────┬─────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ MAGNIFICATION-CALIBRATED GPU POST-PROCESSING │ │ 1. Morphological Erosion (re) on binary logits │ │ 2. Euclidean Distance Transform (sd) │ │ 3. Peak Local Maxima Detection (dm, t_min) │ │ 4. GPU/Multithreaded Watershed Boundary Separation │ │ 5. Small Object Noise Filter (A_min) │ │ 6. Instance Segmentation & Phenotypic Class Fusion │ └───────────────────────────────┬─────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ FINAL INSTANCE CELL SEGMENTATION │ │ • GeoJSON boundaries for QuPath │ │ • Phenotype: Tumor / Immune / Other │ │ • Spatial Connectivity Graph │ └─────────────────────────────────────────────────────────────────┘ ``` ### 1. Stain Deconvolution & The Hematoxylin Difference of Gaussians (DoG) Prior Rather than forcing the neural network to infer stain chemistry from standard RGB values, CP-Net extracts an explicit **nuclear prior channel** through analytical stain separation: 1. **Optical Density (OD) Transformation:** $$\text{OD} = -\log\_{10}\left(\frac{I + 1}{I\_0}\right)$$ where $I\_0 = 240$ represents the background transmitted light intensity, and the constant $1$ avoids logarithmic singularity on saturated pixels. Pixels with low optical density ($\text{OD} < \beta = 0.15$) are excluded as transparent background. 2. **Eigenvector Plane & Percentile Angular Projection:** The covariance matrix $\Sigma = \text{Cov}(\text{OD}*{\text{hat}}^T)$ is decomposed into eigenvalues and eigenvectors. Pixels are projected onto the primary stain plane, and angular coordinates $\phi = \arctan2(T\_y, T\_x)$ are calculated. The 1st and 99th percentiles of $\phi$ establish the robust boundary vectors for Hematoxylin ($v*{\text{min}}$) and Eosin ($v\_{\text{max}}$). 3. **Stain Matrix Decomposition & Normalization:** Stain concentrations $C$ are derived via least-squares minimization: $$C = \arg\min\_C | \text{OD} - M\_{\text{stain}} \cdot C |*2^2$$ The first concentration row $C\_H$ represents hematoxylin. It is normalized against the 99th percentile: $$C*{H,\text{norm}} = \frac{C\_H}{C\_{H,99} / C\_{H,\text{ref}}}$$ where $C\_{H,\text{ref}} = 1.9705$ is the standardized reference maximum hematoxylin concentration. 4. **Multi-Scale Difference of Gaussians (DoG):** The grayscale hematoxylin image $I\_H$ is convolved with 2D Gaussian kernels across smoothing scales $\sigma$: $$G\_\sigma(x, y) = \frac{1}{2\pi \sigma^2} \exp\left(-\frac{x^2 + y^2}{2\sigma^2}\right)$$ $$D\_\sigma = (I\_H \* G\_\sigma) - (I\_H \* G\_{k\sigma})$$ with scale multiplier $k = \sqrt{2}$. The final nuclear prior map takes the maximum DoG response across a calibrated scale set $\Sigma$: $$D(x) = \max\_{\sigma \in \Sigma} D\_\sigma(x)$$ * **$40\times$ Magnification ($0.25\ \mu\text{m/px}$):** $\Sigma = {2.5, 3.5, 4.5}$ * **$20\times$ Magnification ($0.50\ \mu\text{m/px}$):** $\Sigma = {1.6, 2.4, 3.2}$ The resulting single-channel DoG response is concatenated with the RGB image, forming a **4-channel input tensor** $\[B, 4, 256, 256]$ that strongly highlights chromatin texture while suppressing eosinophilic stroma and background void. *** ### 2. Neural Architecture: UniRepLKNet-N Backbone & Dual Decoder Existing pipelines either deploy standard CNNs (HoVer-Net) that lack sufficient contextual receptive fields, or vision transformers (CellViT, CellDETR) that incur quadratic memory overheads ($\mathcal{O}(N^2)$) and high FLOP counts: * **Encoder:** CP-Net uses **UniRepLKNet-N**, a modern convolutional backbone that features large-kernel depthwise convolutions (e.g., $13\times 13$ to $31\times 31$ kernels reparameterized into small kernels during inference). This provides an ultra-wide effective receptive field (ERF) matching ViTs while retaining convolutional translation equivariance, low memory consumption, and extreme throughput on standard GPUs. * **Decoder:** A symmetrical U-Net decoder with multi-scale skip connections merges high-level semantic abstractions with low-level spatial contours. * **Dual Output Heads:** 1. *Binary Nuclei Detection Head:* Predicts a single-channel segmentation logit separating nuclei from background stroma. 2. *Nuclei Type Classification Head:* Predicts a 4-channel tensor corresponding to background, tumor cells, immune cells, and other stromal/epithelial cells. *** ### 3. Multi-Task Objective Function The network is trained end-to-end using a joint detection and classification loss: $$\mathcal{L}*{\text{total}} = \mathcal{L}*{\text{cls}} + \mathcal{L}\_{\text{det}}$$ Where the classification and detection objectives are formulated to overcome class imbalance across dense cellular populations: $$\mathcal{L}*{\text{cls}} = \lambda\_1 \mathcal{L}*{\text{CE}} + \lambda\_2 \mathcal{L}*{\text{Dice}} + \lambda\_3 \mathcal{L}*{\text{FocalTversky}}$$ $$\mathcal{L}*{\text{det}} = \lambda\_4 \mathcal{L}*{\text{Dice}} + \lambda\_5 \mathcal{L}\_{\text{FocalTversky}}$$ Focal Tversky loss specifically penalizes false negatives (missed small immune cells) in dense clusters, preventing the network from biasing exclusively toward large, hyperchromatic tumor nuclei. *** ### 4. Magnification-Calibrated GPU Watershed Post-Processing A notorious failure mode in instance segmentation is the merging of abutting nuclei in crowded tumor nests. Rather than relying on computationally sluggish CPU watershed or complex horizontal-vertical distance fields (HoVer-Net), CP-Net optimizes a **6-step GPU-accelerated morphological pipeline**: 1. **Morphological Erosion ($r\_e$):** Separates weakly connected borders and strips single-pixel boundary noise. 2. **Euclidean Distance Transform ($s\_d$):** Maps distance from nuclear boundaries to centroids. 3. **Peak Local Maxima ($d\_m, t\_{\text{min}}$):** Extracts internal centroid seeds while filtering noise peaks. 4. **Watershed Segmentation:** Propagates boundaries from peak seeds along the inverted distance topography. 5. **Small Object Filter ($A\_{\text{min}}$):** Discards fragments below minimum biological area. 6. **Phenotypic Mask Fusion:** Assigns the majority classified cell type within each watershed polygon. #### Hyperparameter Optimization via Grid Search (200 Expert-Curated Masks) The hyperparameters were formally optimized against 200 diverse ground-truth masks (100 at $20\times$, 100 at $40\times$) using an absolute count discrepancy objective: $$\mathcal{L}*{\text{opt}} = \frac{1}{N} \sum*{i=1}^N |Y\_i - \hat{Y}\_i|$$ | Parameter | Description | $20\times$ Setting ($0.50\ \mu\text{m/px}$) | $40\times$ Setting ($0.25\ \mu\text{m/px}$) | | --------------------- | ------------------------ | :-----------------------------------------: | :-----------------------------------------: | | **$r\_e$** | Erosion radius | **1** | **2** | | **$s\_d$** | Distance mask kernel | **$5 \times 5$** | **$5 \times 5$** | | **$d\_m$** | Minimum peak distance | **4** | **8** | | **$t\_{\text{min}}$** | Peak threshold cutoff | **2** | **2** | | **$A\_{\text{min}}$** | Minimum object area (px) | **10** | **20** | *** ## The \[\[CellQuant-Net]] Clinical WSI Quantification Framework To bridge the gap between tile-level algorithmic benchmarks and gigapixel clinical whole-slide workflows, the authors wrapped CP-Net into **CellQuant-Net**, an end-to-end four-stage pipeline: ```typescript ┌────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ CELLQUANT-NET WSI WORKFLOW │ └────────────────────────────────────────────────────────────────────────────────────────────────────────┘ [ Whole-Slide Image (SVS / NDPI / TIFF) ] │ ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ STAGE 1: WSI Quality Assessment (QA) & Artifact Exclusion │ │ • Deep segmentation backbone (DHUnet / Swin / ConvNeXt) │ │ • Excludes: Blur, Folds, Knife Lines, Pen Ink, Void │ │ • Outputs: WSI thumbnail, *_seg.png heatmap, WSI_Summary.xlsx │ └──────────────────┬──────────────────────────────────────────────────┘ │ High-Quality Tissue Tiles Only ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ STAGE 2: Prior-Guided CP-Net Cell Detection & Classification │ │ • Macenko H-channel deconvolution + DoG prior calculation │ │ • UniRepLKNet-N inference (batch size 128) │ │ • GPU watershed instance segmentation │ │ • Classified: Tumor / Immune / Other │ └──────────────────┬──────────────────────────────────────────────────┘ │ Instance Coordinates & Classes ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ STAGE 3: Spatial Neighborhood Connectivity Graphs │ │ • Radius-based nearest neighbor graph construction │ │ • Cell-cell spatial interaction matrix │ │ • Outputs: connectivity_edges.csv │ └──────────────────┬──────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ STAGE 4: Clinical Biomarker Quantification & QuPath Integration │ │ • Tumor-Infiltrating Lymphocyte (TIL) density & percentages │ │ • Native export: qupath_cells.geojson │ │ • Native export: qupath_cells_connectivity.geojson │ │ • Drag-and-drop interactive review inside QuPath │ └─────────────────────────────────────────────────────────────────────┘ ``` *** ## Multi-Center Evaluation & Benchmark Datasets The study assembled **8 diverse datasets encompassing \~10.4 million annotated nuclei** across different organs, scanners, and resolutions: | Dataset | Primary Organ | Magnification | Scanner | Reference / Source | | ---------------- | ------------------------ | :-----------: | ------------------------------ | ------------------------------------------------------- | | **PanNuke** | Multi-organ (19 tissues) | $40\times$ | Multi-scanner | Gamper et al., 2019 (Used for CP-Net training) | | **CoNSeP** | Colorectal | $40\times$ | Omnyx VL120 | Graham et al., 2019 | | **Lizard** | Colorectal | $20\times$ | Mixed | Graham et al., 2021 (7,429 tiles) | | **NuCLS** | Breast | $20\times$ | Mixed | Amgad et al., 2021 | | **PanopTILs** | Breast | $40\times$ | Mixed | Saltz et al. / TCGA | | **SegPath** | Multi-organ | $40\times$ | Hamamatsu NanoZoomer S60 | Ding et al., 2023 | | **TNMI-20×** | Non-Small Cell Lung | $20\times$ | 3DHistech Pannoramic Flash III | Rakaee et al., 2023 | | **TNMI-40×** | Non-Small Cell Lung | $40\times$ | 3DHistech Pannoramic Flash III | Rakaee et al., 2023 | | **ILCD** *(New)* | Hepatocellular Carcinoma | $40\times$ | Imperial College London | Jabar et al., 2026 (40 HCC WSIs, pathologist annotated) | *Unified 3-Class Taxonomy:* Because raw datasets use incompatible annotation labels (ranging from 4 to 14 classes), all labels were standardized into **Tumor**, **Immune**, and **Other** cells, enabling the first equitable, cross-dataset head-to-head evaluation in the literature. *** ## Empirical Results & Head-to-Head Comparisons ### 1. Cross-Dataset Generalization vs. 10 State-of-the-Art Pipelines CP-Net was compared against 10 published pipelines: **CellViT**, **CellViT++**, **Cerberus**, **HoVer-Net**, **NuLite**, **PointNu-Net**, **CellDetr**, **CellSAM** (detection only), **LKCell**, and **TSFD-Net**: * **Cross-Dataset Performance Spread:** Across external test cohorts, average Panoptic Quality (PQ) ranged widely from **0.05 to 0.34**, Detection Quality (DQ) from **0.07 to 0.46**, and Segmentation Quality (SQ) from **0.22 to 0.62**. This underscores severe domain shifts caused by scanner optical profiles, stain variations, and tissue types. * **Top Performers:** Across all external datasets, **CP-Net, CellViT, and NuLite consistently achieved the highest average PQ and DQ scores**. * **Impact of Scanner Profiling:** Performance was highest on the Omnyx VL120 scanner (CoNSeP) and lowest on heterogeneous, multi-center scanner sets, proving that optical transfer functions remain a major source of out-of-distribution dropoff. * **Phenotypic Disparity:** All models achieved significantly higher PQ on **tumor cells** (which are larger, hyperchromatic, and architecturally distinct) than on **immune cells** (small, hyper-dense, and easily blurred into stromal fibroblasts) or **other stromal cells**. * **Resolution Sensitivity:** Models evaluated at $40\times$ achieved systematically superior panoptic quality compared to $20\times$, reflecting finer nuclear membrane and chromatin texture visibility. *** ### 2. Runtime & Pareto Frontier Analysis ``` Panoptic Quality (PQ) ▲ │ [CellViT] (High PQ, Heavy / Slow) │ ▲ │ ╱ │ [CP-Net] ── (Pareto Optimal: High PQ, Fast / Lightweight) │ ▲ │ ╱ │ [CellDETR] │ │ [HoVer-Net] [PointNu-Net] │ [NuLite] [TSFD-Net] │ (Sub-optimal efficiency trade-offs) └────────────────────────────────────────────────────────► Inference Latency / Cost ``` The study evaluated runtime across gigapixel WSIs on an NVIDIA GPU workstation: #### Table 1: Real-World Whole-Slide Execution Time (Minutes) | Whole-Slide Image | Resolution / Dimensions | CellViT | CP-Net | CellQuant-Net (Complete Pipeline) | | ---------------------- | --------------------------------- | :------: | :--------------------------: | ------------------------------------------------------ | | **WSI 1 ($20\times$)** | $31,871 \times 25,199\ \text{px}$ | 5.15 min | **2.16 min** *(2.4× faster)* | **5.77 min** (QA: 2.67m; CP-Net: 2.16m; Post: 0.94m) | | **WSI 2 ($40\times$)** | $44,125 \times 70,507\ \text{px}$ | 8.30 min | **2.77 min** *(3.0× faster)* | **16.83 min** (QA: 12.58m; CP-Net: 2.77m; Post: 1.48m) | *Takeaway:* While CellViT achieves marginal PQ improvements in certain isolated settings, CP-Net cuts whole-slide cell inference time by **$58%$ to $67%$**, making dense whole-slide single-cell analysis clinically practical on standard hardware. *** ### 3. Clinical Proof-of-Concept: Overall Survival in Hepatocellular Carcinoma To demonstrate clinical validity, the authors applied CellQuant-Net to **13 H\&E WSIs from the AB-real hepatocellular carcinoma (HCC) cohort** with matched clinical outcomes: * Automated WSI-QA excluded pen markings, capsule tears, and blur. * CP-Net quantified tumor-infiltrating lymphocyte (TIL) density across viable tumor beds. * Patients stratified by median TIL density into High vs. Low TIL groups revealed a **statistically significant survival benefit for high TIL infiltration ($p = 0.006$, log-rank test)**. * While preliminary, this confirms that CP-Net generates clinically and biologically meaningful prognostic readouts directly from routine H\&E glass slides without expensive multiplex IHC or transcriptomics. *** ## Comparison with Vault Cell Segmentation Engines | Feature / Model | **CellPrior-Net (CP-Net)** | **\[\[HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides]]** | **\[\[NuClick]]** | **\[\[HoVer-NeXt]]** | **\[\[CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification]]** | | ------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------------------- | ----------------------------------------------- | ---------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | | **Primary Focus** | Fast WSI detection + 3-class classification | Pan-cancer 13-class instance segmentation | Interactive / prompted point-to-mask | Unprompted WSI nuclear instance segmentation | Molecularly supervised single-cell classification | | **Backbone** | UniRepLKNet-N (Large-kernel CNN) | H0-mini (Distilled ViT, 86M) | Lightweight CNN / U-Net | NeXt-Backbone (ConvNeXt / ViT) | ViT-Giant (UNI2-h backbone, 1536-d) | | **Input Channels** | **4-channel:** RGB + Hematoxylin DoG prior | 3-channel RGB | **5-channel:** RGB + Target Inc + Neighbor Exc | 3-channel RGB | $56 \times 56\ \mu\text{m}$ RGB crop ($224 \times 224$ px) | | **Stain Prior** | Explicit Macenko $H$-channel DoG filter | Implicit foundation feature maps | None (Prompt clicks act as spatial priors) | Implicit HV distance fields | Spatial transcriptomics marker supervision | | **Cell Taxonomy** | 3 classes (Tumor, Immune, Other) | 13 classes (Granular immune & stromal) | Generic nuclear boundary | 5–6 generic classes | 23 classes (7 pan-tissue + 16 organ-specific) | | **Post-Processing** | Calibrated GPU watershed ($r\_e, d\_m, A\_{\text{min}}$) | CellViT HV regression & post-processing | Gaussian exclusion thresholding | Energy-landscape horizontal-vertical watershed | Per-organ linear head routing | | **WSI Artifact QA** | **Yes (Built-in WSI-QA module)** | External tiling / QuPath integration | Relies on user click placement | Relies on external tiling filter | Relies on upstream cell segmenter | | **Primary Role** | High-throughput clinical WSI quantification | High-granularity TME phenotyping | Ground-truth dataset curation & active learning | Automated pan-cancer segmentation | High-precision single-cell molecular typing | *** ## Practical Implementation & QuPath Workflow CellQuant-Net is implemented in PyTorch and available at [Falah-Jabar-Rahim/CellQuant-Net](https://github.com/Falah-Jabar-Rahim/CellQuant-Net): ### 1. Installation & Environment ```bash git clone https://github.com/Falah-Jabar-Rahim/CellQuant-Net.git cd CellQuant-Net chmod +x install.sh ./install.sh conda activate cellquantnet python verify_installation.py ``` ### 2. End-to-End Whole-Slide Inference CLI Place `.svs` or `.ndpi` slides in `input/`, download weights into `CP-Net/weights/` and `WSI_QA/pretrained_ckpt/`, and execute: ```bash python run_cellquant_net.py \ --cpu_workers 32 \ --batch_size 128 \ --cell_connectivity \ --model_type PanNuke.pth ``` Available pretrained checkpoints include: * `PanNuke.pth`: Multi-organ ($40\times$) * `SegPath.pth`: Multi-organ ($40\times$) * `CoNSeP.pth`: Single-organ Colon ($40\times$) * `Lizard.pth`: Single-organ Colon ($20\times$) * `ILCD.pth`: Single-organ Liver ($40\times$) * `NuCLS.pth`: Single-organ Breast ($20\times$) * `PanopTILs.pth`: Single-organ Breast ($40\times$) * `TNMI20x.pth` / `TNMI40x.pth`: Single-organ Lung ($20\times$ / $40\times$) ### 3. QuPath Interactive Review CellQuant-Net generates zero-friction visualization artifacts directly inside `output/CP_Net//`: 1. Open the analyzed whole-slide image in **QuPath**. 2. Drag and drop `qupath_cells.geojson` into the QuPath window to render all classified cellular polygons (Tumor, Immune, Other). 3. Drag and drop `qupath_cells_connectivity.geojson` to visualize the spatial cell-cell interaction graph. 4. Inspect `WSI_Summary.xlsx` and `cell_type_stats.csv` for summary infiltration metrics. *** ## Vault Navigation & Related Notes * **Dedicated Tool Profile:** \[\[CellQuant-Net]] * **Cell Instance Segmentation & Foundation Models:** \[\[HistoPLUS: Towards Comprehensive Cellular Characterisation of H\&E Slides]], \[\[NuClick]], \[\[CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification]], \[\[HoVer-NeXt]], \[\[HistoGen: Histopathology Cell Nuclei Image Generation Tool]] * **Software Ecosystem & Tools:** \[\[Digital Pathology Software]], \[\[Digital Pathology]], \[\[Image Analysis]], \[\[From Samples to Knowledge 2025: QuPath Training Course]], \[\[Multiplex Immunofluorescence Image Analysis with QuPath — Part 1: Understanding Digital Images]] * **Literature Reviews:** \[\[Articles on computational, digital, and mathematical pathology]], \[\[Navigating foundation model selection in digital pathology through performance evaluation and tradeoff analysis]], \[\[A Hybrid MIL Approach Leveraging Convolution and State-Space Model for Whole-Slide Image Cancer Subtyping]]