Computational Pathology

Follow tissue from fixation and sectioning to a stained glass slide. Compare hematoxylin and eosin sections with common special stains and recognize how preparation changes what can be seen.

Recognize inflammation, injury, infection, and neoplasia as patterns in tissue. Use a small set of slide cases to connect visible findings with a diagnostic question.

Trace biopsies and resections through collection, accessioning, processing, and slide production. Practice checking specimen identity, orientation, and chain of custody, including the safety steps that protect staff and patients.

Read the clinical question, specimen description, diagnosis, and comments in a pathology report. Practice matching a reported finding to the slide evidence and noting when the available material limits a conclusion.

Follow the move from glass-slide microscopy to telepathology, whole-slide imaging, and computational analysis. See how scanner performance, storage, clinical regulation, and advances in machine learning shaped current practice.

See how scanners capture slides at different magnifications and why focus, resolution, throughput, and color calibration matter. Compare scans of the same slide for diagnostic and computational use.

Navigate whole-slide images, select regions of interest, and record annotations without losing slide context. Practice a structured digital review using normal and abnormal tissue.

Work with image pyramids, coordinates, tiles, thumbnails, and slide metadata. Read common whole-slide formats and see how DICOM supports image exchange between clinical systems.

Detect blur, folds, missing tissue, staining problems, and scan failures before analysis. Build a slide-level quality checklist and decide when rescanning or exclusion is warranted.

Use Python to open slides, extract tiles, visualize annotations, and save results. Keep code, data versions, and analysis settings organized so another person can reproduce the work.

Prepare image regions for analysis through tissue detection, color handling, resizing, and stain normalization. Test whether each step fixes a real variation or removes useful diagnostic information.

Use sampling, uncertainty, confidence intervals, and diagnostic test measures to describe pathology results. Calculate sensitivity, specificity, and predictive value for a concrete screening task.

Define the intended task, sampling unit, reference diagnosis, and annotation instructions. Create patient-separated training and test sets while checking for duplicate tissue, site effects, and label disagreement.

Measure color, texture, shape, and spatial arrangement in image regions. Build a simple feature-extraction pipeline and inspect where its measurements fail on real slides.

Train and assess conventional classifiers using measured image features. Compare interpretable baselines with more flexible methods while controlling overfitting and data leakage.

Train convolutional and other image models on pathology tiles, with attention to augmentation, class imbalance, and compute needs. Relate tile predictions back to the source slide.

Identify nuclei and cells, separate touching objects, and evaluate boundaries against expert annotations. Turn segmentation masks into counts and measurements while tracking common errors.

Map tissue compartments and histologic patterns across a whole slide. Combine region annotations with model predictions to produce a reviewable tissue map.

Make slide-level predictions when only slide-level diagnoses are available. Build a multiple instance learning pipeline and inspect which regions drove its result.

Measure how cells and tissue regions are distributed relative to one another. Use neighborhood and distance analyses to test a question about tissue architecture rather than relying on counts alone.

Use representations learned from large collections of unlabeled pathology images for smaller downstream tasks. Compare foundation-model features with task-specific training and test transfer across scanners and sites.

Connect pathology images with report text for retrieval, description, and question-answering tasks. Evaluate whether a vision-language model’s statements are supported by the slide and suitable for clinical review.

Study image synthesis, virtual staining, and synthetic training data as distinct applications of generative models. Check generated images for invented structures, privacy risks, and changes to diagnostic evidence.

Train across institutions without pooling raw slides in one place. Compare federated learning with shared-data training and account for differences in scanners, stains, diagnoses, and patient populations.

Measure biomarker staining in the right cells and tissue regions, then relate scores to pathologist review. Practice scoring an immunohistochemistry case while accounting for controls and staining artifacts.

Identify cell types and marker combinations in multiplex tissue images. Build cell-level maps and assess how registration and signal quality affect conclusions about the local tissue environment.

Connect spatial gene-expression measurements to their locations in tissue. Interpret resolution, capture limitations, and tissue morphology together in a small spatial analysis.

Connect histologic findings with mutation, expression, and other molecular test results. Distinguish what an image suggests from what a validated molecular assay establishes.

Test whether slide images predict molecular features or treatment-relevant markers. Examine confounding, low-prevalence targets, and the evidence needed before an image-based prediction could guide testing.

Combine slide-derived features with molecular results, clinical records, or radiology for a defined prediction task. Handle missing inputs and check whether the combined model improves on each source alone.

Apply image analysis to cell preparations used in screening and diagnosis. Work through cell detection, specimen adequacy, and review thresholds in a cytology case.

Analyze blood films and bone marrow images while accounting for rare cells and variable preparations. Compare automated counts and classifications with expert review.

Apply computational methods to tumor detection, grading, margin assessment, and measurement in tissue resections. Tie model outputs to the decisions and limitations of a surgical pathology case.

Follow slides and results through laboratory information systems, image management, viewers, and reporting software. Map identifiers and interfaces so an analysis result reaches the correct case.

Work through remote consultation and primary diagnosis using digital slides. Address image access, case assignment, documentation, jurisdictional rules, and the limits of remote review.

Present model findings so a pathologist can inspect the underlying tissue and disagree with the system. Test how explanations, uncertainty, and interface design change decisions in a review task.

Place image analysis inside a real case review without treating a model output as a diagnosis. Practice triage, discrepancy resolution, escalation, and recording who made the final decision.

Evaluate a model against its intended clinical use, comparator, and reference standard. Design retrospective and prospective studies that measure errors, workflow effects, and patient-relevant outcomes.

Test performance on patients and slides from other sites, scanners, stains, and demographic groups. Investigate failures, report subgroup results, and decide when a model is not ready to transfer.

Develop a quantitative pathology measure from an analytical result into a clinically useful biomarker. Examine thresholds, reproducibility, study design, and the additional evidence required for companion diagnostics.

Handle identifiable slides and linked clinical data under consent, access, retention, and data-sharing rules. Apply privacy safeguards to a realistic multi-institution study, including applicable HIPAA requirements.

Protect scanners, slide stores, software dependencies, and model services from unauthorized access or tampering. Make a threat checklist for a connected pathology pipeline and plan a response to a suspected breach.

Distinguish research software from tools used for clinical decisions. Examine applicable FDA pathways, local professional rules, documentation, and the responsibilities of the laboratory and signing pathologist.

Apply laboratory quality management to digital slides and computational results. Work through validation records, change control, proficiency expectations, and relevant CLIA, CAP, and local accreditation requirements.

Package a validated model for dependable use, then monitor image quality, data drift, errors, and service uptime. Practice versioning and rollback when a scanner or model change affects results.

Take a clinical question from specimen and data selection through annotation, modeling, external validation, laboratory approval, system integration, pathologist review, and follow-up monitoring. Deliver a documented decision on whether and how the tool should be used.

Compare clinical, laboratory, research, engineering, and regulatory roles in computational pathology. Plan an entry route with a slide-analysis portfolio, relevant credentials or supervised training, and habits for tracking evidence and standards.