INTELLIGENT DIGITAL PATHOLOGY

See pathology differently.

Smart Pathology is an intelligent platform for pathology image upload, high-resolution viewing, AI-assisted analysis, structured reporting, and traceable analysis history — developed and implemented by Pooya Pardazan Asre Hooshmand.

See the Platform in Action → Explore AI Models

40×interactive viewer zoom

4focused AI workflows

SVS · TIFF · PNGmulti-format pathology imaging

Real Smart Pathology interface

PLATFORM CAPABILITIES

One platform. From slide to insight.

The workflow follows the real sequence of digital pathology work: upload the case, inspect the slide, select the analysis, run the model, review the overlays, generate the report, and revisit the analysis later.

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Multi-format upload

Upload standard images and whole-slide images including SVS, TIFF/TIF, PNG, JPG/JPEG, NDPI, SCN, MRXS, VMS/VMU and related formats.

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Interactive slide viewer

Navigate large images, inspect regions of interest, work with tiles, and review the slide at up to 40× magnification.

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Task-oriented AI

Choose tissue classification, tissue segmentation, or nucleus analysis, then select the appropriate model for the task.

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Reports & analysis history

Review visual overlays, quantitative findings, structured reports, and a persistent record of completed analyses.

SMART PATHOLOGY IN ACTION

A real workflow, shown step by step.

These are real screenshots from the working Smart Pathology platform. The slideshow follows a case from the dashboard and upload page to model selection, AI execution, and final reports.

Smart Pathology · localhost demo

SYSTEM ARCHITECTURE

A modular path from image to report.

Image management, high-resolution viewing, analysis orchestration, AI execution, result composition, reporting, and history storage are separated into clear functional stages.

Upload image

Attach the pathology image to a named case.

Validate & ingest

Check format, metadata, dimensions, and WSI compatibility.

Open viewer

Render the image or slide using a tiled multi-resolution viewer.

Choose task & model

Select classification, segmentation, or nucleus analysis and the related model.

Run AI analysis

Execute inference and model-specific post-processing.

Review outputs

Inspect overlays, tissue percentages, classes, cell information, and findings.

Report & archive

Store the report, outputs, model information, and complete analysis history.

IMAGE REPOSITORY
Original slide · ROIs · tiles · thumbnails

AI ORCHESTRATION LAYER
Task selection · model routing · inference · post-processing · result composition

ANALYSIS ARCHIVE
Reports · overlays · findings · timestamps · model metadata

SELECTED AI MODELS

Pathology intelligence from tissue to nucleus.

Each model is presented with a dedicated pathology visual, the target output classes, and the relevant training-data context — similar to the model slides used in the presentation.

TumorStromaImmuneMucus

Tissue classification

Colorectal Tissue Classification

The classifier works on tissue patches and identifies the dominant colorectal tissue pattern. In the platform, these predictions can be used to build a regional tissue map and summarize the tissue composition of the selected image area.

Output classes

Colorectal adenocarcinoma epithelium

Normal colon mucosa

Cancer-associated stroma

Lymphocytes

Smooth muscle

Adipose tissue

Mucus

Debris / tissue remnants

Background / empty slide area

Kather100K100,000 H&E patches86 slides9 classes

TumorStromaInflammationNecrosis

Semantic segmentation

Breast Cancer Tissue Segmentation

This model produces pixel-level tissue masks rather than a single label. The platform can render those masks directly over the original slide and calculate the approximate area and proportion of each tissue compartment.

Output classes

Tumor

Stroma

Inflammatory tissue

Necrosis

Other tissue: nerve, vessels, blood cells, adipose and related structures

BCSS>20,000 annotations151 breast cancer casesMean Dice 0.763

BasalCore epitheliumKeratinNuclei

Multi-task analysis

Oral Epithelial Dysplasia Analysis

This model combines two levels of analysis: semantic segmentation of the oral epithelium and instance-level analysis of nuclei. This makes the output closer to pathology review, where both tissue architecture and cellular morphology are considered together.

Output classes

Basal epithelium

Core epithelium

Keratin

Other tissue

Background

Epithelial nuclei vs other nuclei

43 WSIs38 OED + 5 healthyLayer F1 ≈ 0.82Nuclei Dice ≈ 0.84

NeoplasticInflammatoryConnectiveEpithelial

Nucleus segmentation & typing

Nucleus Segmentation and Cell-Type Analysis

Instead of describing the tissue region, this model identifies individual nuclei, separates touching nuclei, and assigns them to clinically meaningful nucleus categories. The result can support cell counting, density analysis, and spatial characterization of the microenvironment.

Output classes

Neoplastic / tumor nuclei

Non-neoplastic epithelial nuclei

Inflammatory nuclei

Connective-tissue nuclei

Dead / necrotic nuclei

PanNuke≈200,000 nuclei19 tissue types5 nucleus classes

OUTPUTS & TRACEABILITY

Visual evidence plus measurable results.

The platform keeps the pathologist in control while making the AI result transparent, reviewable, quantitative, and easy to revisit.

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Visual overlays

Masks, nucleus contours, tissue labels, and color-coded regions are placed directly over the pathology image.

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Quantitative findings

Tissue percentages, cell counts, class distributions, and other task-specific measurements are summarized for review.

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Structured reports

The report combines slide metadata, selected model, analysis findings, technical context, and review guidance.

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Persistent history

Completed analyses are retained so users can reopen the case, compare outputs, or generate the report again.

From digital slide to intelligent evidence.

Smart Pathology is designed to support pathology specialists with AI-assisted visual evidence, quantitative information, and a reproducible digital workflow — while keeping expert interpretation at the center of the process.

See the Workflow