Course materials for Machine Learning for Biomedical Imaging (MLBI), taught at UPC Barcelona and developed at B2SLab / IRIS-UPC.
Each module below links to the lecture presentation and any interactive dashboards built to accompany it. Dashboards open in the browser and require no installation.
Module 1 — Introduction to Neural Networks
Biological inspiration for neural networks, the perceptron model, activation functions, loss landscapes, and the basics of learning. Covers the historical context from Hebbian learning to modern multi-layer architectures.
Module 2 — Multilayer Perceptrons and Learning
Forward and backward pass through multi-layer perceptrons, the chain rule, gradient descent, overfitting and regularisation, and model evaluation.
Open presentation PDF
Interactive dashboards
Backpropagation Explorer
Step through the forward and backward pass of a small network. Inspect activations, gradients, and weight updates at each layer interactively.
Open dashboard
Module 3 — Convolutional Neural Networks
Convolution as a mathematical operation, the convolution theorem, 2-D convolutions for image processing, pooling, and the architecture of modern CNNs for biomedical image analysis.
Open presentation PDF
Interactive dashboards
1-D Convolution Explorer
Explore how a 1-D convolution kernel slides over a signal, adjust kernel values, and observe the resulting feature map in real time.
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2-D Convolution Explorer
Apply user-defined kernels to 2-D images. See how edge-detection, blurring, and sharpening kernels transform the input.
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Convolution Theorem Explorer
Visualise the equivalence between convolution in the spatial domain and element-wise multiplication in the frequency domain.
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FFT Explorer
Decompose signals into their frequency components using the Fast Fourier Transform. Observe amplitude spectra and the effect of filtering.
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Module 4 — Recurrent Neural Networks and Sequence Modelling
Sequence modelling with RNNs, vanishing gradients, LSTM and GRU cells, bidirectional architectures, and biomedical applications including ECG classification, EEG seizure detection, and protein secondary structure prediction.
Open presentation PDF
Interactive dashboards
RNN Unit — Forward Pass
Animated walkthrough of a single RNN cell processing a sequence step by step. Inspect hidden state updates at each time step.
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RNN Unit — Forward & Backward Pass
Extends the forward-pass animation with backpropagation through time (BPTT), showing how gradients flow (and vanish) across time steps.
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RNN — Protein Secondary Structure (forward pass)
A toy RNN trained to predict protein secondary structure. Follow the forward pass residue by residue and observe the predicted class at each position.
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GRU — Protein Secondary Structure Prediction
Same protein secondary structure task, now solved with a Gated Recurrent Unit. Compare predictions and gating behaviour against the plain RNN demo.
Open dashboard
Slides are built with Quarto and Reveal.js. Dashboards are self-contained HTML files requiring only a modern browser.