Whole-Slide Prediction Basics

This vignette walks through a whole-slide style inference workflow using a pretrained detection model. The focus is:

  • loading a pretrained model
  • running sliced inference on a large image
  • reviewing overlays and tabular outputs
  • summarizing detections for downstream analysis

For now this is detection-first. Segmentation can also be run on large images, but its workflow is different because RF-DETR segmentation does not yet use the SAHI path in this package.

Load the Toolkit

library(petrographer)
library(dplyr)
library(fs)

Point to a Large Image

Replace the placeholder path with a whole-slide image or any large thin-section image you want to analyze.

wsi_path <- "path/to/whole_slide_image.tif"
file_exists(wsi_path)

Load a Pretrained Model

Use from_pretrained() to load a detector from your local board or the public hub.

model <- from_pretrained(
  model_id = "inclusions_small",
  device = "cpu",
  confidence = 0.5
)

model

The loaded object keeps the parsed manifest and training summary, so you can inspect provenance if needed:

model$manifest$model
model$training_summary$training

Run SAHI Inference

For large images, sliced inference usually works better than trying to process the entire image at once. slice_size and overlap let you trade off runtime, memory use, and boundary stability.

output_dir <- path("results", path_file(path_ext_remove(wsi_path)))

predictions <- predict_image(
  image_path = wsi_path,
  model = model,
  use_slicing = TRUE,
  slice_size = 1024,
  overlap = 0.2,
  output_dir = output_dir,
  save_visualizations = TRUE
)

predictions

The returned tibble contains one row per detected object, including class labels, confidence scores, bounding-box-derived geometry, and summary fields added by enhance_results().

Review the Overlay

dir_ls(output_dir)

viz_path <- dir_ls(output_dir, glob = "*_prediction.png", fail = FALSE)
if (length(viz_path)) {
  knitr::include_graphics(viz_path[[1]])
}

Summarize Detections

per_image <- summarize_by_image(predictions)
per_image

population <- get_population_stats(predictions)
population

These summaries are useful for:

  • slide-level QA
  • comparing slides or treatment groups
  • spotting large shifts in count or size distributions

Batch Process a Directory

Once you are happy with the settings on one image, scale out to a directory.

batch_results <- predict_images(
  input_dir = "path/to/whole_slide_directory",
  model = model,
  use_slicing = TRUE,
  slice_size = 1024,
  overlap = 0.2,
  output_dir = "results/batch_whole_slides",
  save_visualizations = TRUE
)

batch_results

Next Steps

  • adjust slice_size and overlap to match your image scale and memory budget
  • filter predictions with tidyverse tools for class-specific summaries
  • use summarize_by_image() and get_population_stats() for reporting
  • for segmentation-oriented workflows, use the training and prediction templates/notebooks rather than this detection-first vignette