Finding Cancer in Ultrasound Images

Finding Cancer in Ultrasound Images#

From pixel logistic regression to transfer learning

BreastMNIST ultrasound — metrics, trade-offs, and Grad-CAM explanations of what each model attends to

Not a medical device. This is an educational computer-vision case study on a public 128×128 benchmark. It is not validated for screening or diagnosis, and it should not be used to interpret clinical images.

Project Overview#

Radiologists reading breast ultrasound look for irregular hypoechoic masses, angular margins, and posterior acoustic shadowing. This project asks how far a sequence of machine-learning models can get on the same task — malignant vs benign/normal — when the images are the public BreastMNIST split of the BUSI ultrasound collection.

The modelling ladder is deliberate. A majority-class dummy sets the accuracy floor (never predicting cancer still scores ~73% because malignancy is the minority class). A linear model on PCA-reduced pixels is next, then classical computer-vision features (HOG and LBP) with a random forest, then a small CNN trained from scratch, and finally a frozen ImageNet ResNet-18 with a new classification head. Every model is scored on the same official test set, with decision thresholds chosen on the validation split.

The interesting part is not a single accuracy number. Precision, recall, specificity, ROC-AUC and PR-AUC move differently as the models get more expressive — and Grad-CAM overlays show whether the conv nets are looking at the mass or at a scan artefact.

Project Structure#

Five notebooks, from the images through to a held-out comparison dashboard:

1. Images & Labels

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Official splits, class balance, montages, and mean-image differences

2. Classical Models

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Dummy classifier, PCA + logistic regression, HOG/LBP random forest

3. Deep Learning

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A small CNN from scratch and a frozen ResNet-18 transfer head

4. Interpretation

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Pixel coefficient maps, texture importances, and Grad-CAM case grids

5. Evaluation

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Precision, recall, ROC/PR, calibration, and which metric to trust

Key Questions#

  • If a model never predicts cancer, how high can accuracy still be — and why is that useless here?

  • Do hand-crafted edge and texture features beat a linear model on flattened pixels?

  • Does a CNN trained on 546 images outperform those classical features, or do we need transfer learning?

  • When Grad-CAM lights up, is it the mass, the acoustic shadow, or a measurement crosshair?

Preview Figures#

Training examples by class

Held-out metric comparison

ResNet-18 Grad-CAM case grid

Dataset#

  • Source: BreastMNIST (MedMNIST v2/v3), derived from the BUSI breast-ultrasound collection

  • Task: binary classification, malignant = positive class (MedMNIST’s 0/1 labels are remapped in the notebooks)

  • Size: 780 images at 128×128 grayscale — 546 train / 78 val / 156 test

  • Licence: MedMNIST CC BY 4.0; cite Yang et al., Scientific Data (2023) and Al-Dhabyani et al., Data in Brief (2020)

  • Bundled file: data/breastmnist_128.npz (no live download at book-build time)

Technical Stack#

Python • MedMNIST • scikit-learn • scikit-image • PyTorch • torchvision • Plotly • Grad-CAM

Ready to look at the images?

Start with Images & Labels →

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