A hands-on tutorial on applying AI to Multiple Myeloma diagnosis from bone marrow smear images, built around two public plasma cell datasets from LabIA-UFBA.
| # | Dataset | Paper | Original Model |
|---|---|---|---|
| 1 | Multiple Myeloma Dataset | Andrade et al. (2024), Scientific Reports 14:11176 | YOLOv7 |
| 2 | PCMMD | Andrade et al. (2025), Scientific Data 12:161 | YOLOv8 |
- Dataset 1 — 1,024 bone marrow aspirate smear images, Wright-Giemsa staining, smartphone-captured, single-class YOLO bounding box annotations, 10-fold validation.
- Dataset 2 (PCMMD) — 10 patients, detection + segmentation annotations, CC-BY-4.0, 5-fold validation.
This tutorial reproduces the original pipelines using YOLO26 (latest Ultralytics release) instead of the original YOLOv7/YOLOv8, favoring fast, live-demoable runs (nano model, few epochs) over state-of-the-art accuracy.
01_image_presentation.ipynb— Downloads both datasets and visualizes images with their YOLO annotations.02_training_yolo.ipynb— Fine-tunes YOLO26n on Dataset 1 (single class), shows training curves, and evaluates on the test set.03_diagnosis.ipynb— Trains a 2-class (plasma / non-plasma) detector on PCMMD, aggregates per-patient detections into a plasma cell percentage, and applies a clinical threshold (>10%) for diagnosis.04_synthetic_data_augmentation.ipynb— Builds a synthetic data pipeline: per-slide statistics (KDE) → cell crops/masks → color deconvolution (Beer-Lambert) → DDPM (diffusion) training → synthetic slide composition → baseline vs. augmented comparison.
Run them in order; each notebook downloads any data it needs if not already present.
pip install -r requirements.txtRequires Python with Jupyter (ipykernel), PyTorch, Ultralytics YOLO, and Hugging Face diffusers for the generative pipeline in notebook 04.
Datasets and model weights are downloaded automatically by the notebooks into data/ and are not tracked in git (see .gitignore). No manual setup is required beyond internet access on first run.
slides/ holds the presentation materials for the tutorial.
If you use these datasets, please cite the original papers:
- Batista Filho, J.L.S., Nogueira Rios, T., Araújo Rios, R.: Synthetic Data Augmentation for Plasmocyte Detection in Bone Marrow Images. In: Brazilian Conference on Intelligent Systems (BRACIS) (2026).
- Andrade, C.L.B., Ferreira, M.V., Alencar, B.M., et al.: PCMMD: A novel dataset of plasma cells to support the diagnosis of multiple myeloma. Scientific Data 12(1), 161 (2025). doi.org/10.1038/s41597-025-04459-1.
- Andrade, C.L.B., Ferreira, M.V., Alencar, B.M., et al.: Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides. Scientific Reports 14(1), 11176 (2024). doi.org/10.1038/s41598-024-61420-9.