My preprocessing code heavily depends on the public notebooks (eg. remove letters, crop breast via contour).
- crop edge (margin pixel 10)
- extract breast with
- resize to 1536x960. (I roughly guess that resizing into a 1.5 ~ 2.0 aspect ratio is fine.)
In my experiment, windowing doesn't affect the score positively, so I decide not to use it.
Heavy augmentation works well. Light augmentation tends to overfit.
- scale / rotate
- brightness / contrast
- cutout (coarse dropout with large patch size)
I couldn't spend much time running various experiments due to a lack of time & computing resources. So, I only tested few backbones & training recipes. (about 70% of my submissions are runtime errors & mistakes lol)
Here's a full pipeline.
- pre-train segmentation model with the
- segment: provided RoI image.
malignantto cancer /
BIRADS 5to cancer.
- train with competition data (initialize the weight with the pre-trained model)
- segment: inferred with the pre-trained model.
- auxiliary: given meta-features (total 11 features).
- re-label the external data with the
- re-train with competition data (initialize with
- train a meta-classifier (oof + meta-features (e.g. laterality, age, ...))
For a baseline, I run step 1 ~ 2, 5 and achieve CV 0.4885 LB 0.59 (PB 0.46). Also, I test only with the
cbis-ddsm dataset for pre-training, and there were about 0.02 drops on CV & LB, but the same score on PB (CV 0.4656 LB 0.57 PB 0.46).
A week before the deadline, I finished up to step ~ 5 and got CV 0.5012 LB 0.55 (PB 0.51). Sadly, I didn't choose it as a final submission : (
Last day of the competition, I ensembled
effnet_v2_s backbone and got CV 0.5063 LB 0.56 (PB 0.49).
Lastly, I choose the best LB & CV for the final submission.
I built a meta-classifier with meta-features like age, laterality, and the (per-breast) statistics of the predictions. Usually, It gives ~ 0.02 improvements on the CV & LB (also PB).
I'm worried about overfitting into some meta-features (eg. machine id, (predicted) density, ...), so I decided to use only 3 auxiliary features (age, site_id, laterality) to train the model.
- feature: age, site_id, laterality, (mean, std, min, max) of the predictions.
- cv: stratified k fold (5 folds)
- model: CatBoost
- higher resolution (1536x768 ~ 1024) is good.
- external data
- it gives about +0.02 boosts.
- encoder: backbone:
- decoder: u-net++
- encoder: backbone:
- mixup (alpha 1.0)
- 0.6 * cls_loss (cross_entropy) + 0.4 * seg_loss (dice) + 0.1 * aux_loss (cross-entropy)
- stratified group k fold (4 folds)
thanks for reading! I hope this could help you :)