---
title: Pathology outputs
description: Tiles, tissue masks, and deep-learning features — the result of each pathology workflow and how the card renders.
---

Pathology analysis returns a compact result summary plus downloadable artifacts, and exactly what you get depends on the **workflow** you ran. Read the summary on the card first — tile count, tissue coverage, or feature dimension — then download the archive, mask, or feature file for downstream work. See [inputs](/docs/pathology/inputs) for the parameters that shape these results.

## Results by workflow

### Tile extraction

- **`num_tiles`** — how many tiles survived the tissue and blur filters.
- **`slide_dimensions`** — the slide's pixel dimensions.
- **Artifact**: `tiles.zip` — an archive of the retained tiles in your chosen `output_format`.

Each retained tile records its position (`x`, `y`), `tissue_percentage`, and `blur_score` internally, so the archive contains only tiles that passed both quality filters.

### Tissue detection

- **`tissue_percentage`** — the fraction of the slide covered by tissue, as a percentage.
- **`num_regions`** — how many distinct tissue regions were found.
- **`regions`** — bounding boxes (`x`, `y`, `width`, `height`, `area`) for each region above the artifact-size threshold.
- **`image_dimensions`** — the analyzed image dimensions.
- **Artifact**: `tissue_mask.png` — the binary tissue mask (when `save_masks` is on).

### Feature extraction

- **`num_tiles`** — tiles from which features were extracted.
- **`feature_dim`** — the feature vector length (2048 for ResNet-50).
- **`mean_features`** — the mean feature vector across tiles.
- **Artifact**: `features.npy` — the per-tile feature matrix (when `save_features` is on).

## Common fields

Every completed run also reports `processing_time_seconds` and uploads a job manifest artifact that records the full run context.

## What the card shows

Results render as an **intelligence card** (a chart-type card) on the canvas. The preview surfaces the headline metrics the analysis produced, which may include:

- **Cells** — cell count, when a workflow reports one (primary metric).
- **Tissue** — the tissue-type classification label.
- **Annotations** — the number of detected regions.
- **Confidence** — a prediction-confidence score, color-coded (green above 90%).

Expand the card to see the full summary and to reach the downloadable artifacts.

## Using the results

- **Tiles** feed directly into feature extraction, or into an external classifier.
- **Tissue masks and coverage** quantify how much usable tissue a slide carries before you invest compute in it.
- **Feature matrices** support clustering, similarity, and classification across tiles or slides.

## Read next