• Original Post : https://www.kaggle.com/competitions/rsna-breast-cancer-detection/discussion/391133

Data

Preprocessing

My preprocessing code heavily depends on the public notebooks (eg. remove letters, crop breast via contour).

1. decode .jpeg with dicomsdl & nvjpeg2000.
2. crop edge (margin pixel 10)
3. extract breast with opencv2 (contour based)
4. 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.

Augmentation

Heavy augmentation works well. Light augmentation tends to overfit.

• v/hflip
• scale / rotate
• brightness / contrast
• cutout (coarse dropout with large patch size)
• mixup

Architecture

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.

1. pre-train segmentation model with the cbis-ddsm & vindr datasets.
   • segment: provided RoI image.
   • label: malignant to cancer / BIRADS 5 to cancer.
   % Of course, the label doesn't perfectly correlate with the competition standards. But, I roughly think that maybe it could help train the model in some ways.
2. 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).
3. re-label the external data with the step 2 model.
4. re-train with competition data (initialize with step 3 model)
5. 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.

Meta-Classifier

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

Works

• higher resolution (1536x768 ~ 1024) is good.
• external data
  • it gives about +0.02 boosts.
• architecture
  • encoder: backbone: effnet-b3 works best
  • decoder: u-net++
• augmentation
• mixup (alpha 1.0)
• loss
  • 0.6 * cls_loss (cross_entropy) + 0.4 * seg_loss (dice) + 0.1 * aux_loss (cross-entropy)
• stratified group k fold (4 folds)
• meta-classifier
• TTA

thanks for reading! I hope this could help you :)