Biomedical Physics with Applications to Disease
BPAD interactive laboratories
Bioelectric and biomagnetic physics, imaging, data modeling, AI and machine learning — from cellular potentials to clinical inference. Chapter 7 laboratories and Chapter 9 companions connect mathematical models with quantitative experiments.
Module clusters
Nine topic clusters connect physical measurement, signal analysis, statistical inference and deployment.
Foundations & Study Design
Estimands, dependence structure, acquisition shift, leakage, and the design decisions that determine whether a model result means anything at all.
5 modules · Chapter 9 · Sections 1–3
Imaging Features & Preprocessing
From CT volumes and masks to quantitative features: physical units, texture discretization, augmentation, and the physics of image formation.
5 modules · Chapter 9 · Sections 4–6; Chapter 8 bridge
Bioelectric & Biomagnetic Methods
Electrogenesis, ECG, EEG, MEG, EMG, volume conduction, inverse estimation, uncertainty and EIT.
10 modules · Chapter 7 · Labs 7.1–7.15
Supervised Learning
Classification and regression on the kidney cohort: thresholds, ROC geometry, neighborhood methods, model comparison, and decision cost.
7 modules · Chapter 9 · Section 7
Time-to-Event & Longitudinal
Censoring, competing risks, landmark analysis, tumor growth kinetics, and joint models for repeated biomarker measurements.
6 modules · Chapter 9 · Section 8
Unsupervised Learning & Segmentation
Phenotyping, cluster stability, intensity and spectral segmentation, and the metrics used to judge a delineation.
6 modules · Chapter 9 · Section 9
Deep Learning & Image-Native AI
Capacity and overfitting, convolution kernels as physical operators, receptive fields, parameter budgets, and multimodal fusion.
4 modules · Chapter 9 · Section 10
Evaluation, Calibration & Uncertainty
Discrimination versus calibration, decision curves, conformal prediction intervals, and reproducibility auditing.
4 modules · Chapter 9 · Section 11
Translation, Fairness & Deployment
Subgroup fairness, counterfactual explanation, shortcut learning, differential privacy, human-AI teaming, and model documentation.
6 modules · Chapter 9 · Sections 12–13
Reproducible by construction
Every module exposes its random seed. Two people who set the same seed see identical numbers, which makes results quotable in a lecture or a problem set.
Physics-aware simulations
Slice thickness, reconstruction kernels, partial-volume averaging and noise are modeled explicitly rather than hand-waved, so the statistical consequences follow from the imaging physics.
Documentation alongside the demo
Each module carries the derivation, the learning objectives, discussion prompts and suggested classroom use next to the interactive figure.