| --- |
| license: cc-by-nc-nd-4.0 |
| tags: |
| - histopathology |
| - whole-slide-image |
| --- |
| |
| # BigCell sample data |
|
|
| This is a small sample of the data format used to train and evaluate |
| [BigCell](https://huggingface.co/StonyBrook-CVLab/BigCell) ([GitHub](https://github.com/cvlab-stonybrook/BigCell)). |
| It contains two 32,768×32,768 regions at 20×: |
|
|
| | Region | Source | |
| |---|---| |
| | `cptac/AML/C3L-00453-41_x0_y0` | CPTAC (AML) | |
| | `tcga_diagnostic/brca/TCGA-3C-AALI-01Z-00-DX1.F6E9A5DF-D8FB-45CF-B4BD-C6B76294C291_x0_y0` | TCGA-BRCA (diagnostic) | |
|
|
| Each region is cut into non-overlapping crops of 2048 to 32768 px, excluding background. The sample has |
| 6.8 GB, 1,600 files. |
|
|
| ## Layout |
|
|
| All crops are at 20×. `<size>` is the side length of the crop in pixels at 20×. |
|
|
| ``` |
| <source>/<type>/<slide>_x<X>_y<Y>/<size>/ size ∈ {2048, 4096, 8192, 16384, 32768} |
| <N>.jpg 2048×2048 image (the crop downsampled when size > 2048) |
| <N>_full.jpg the crop at full resolution (size > 2048) |
| <N>_uni.npy (size/256, size/256, 1536) float16 UNI-2h grid, one token per 256×256 patch |
| <N>_vae.npy (64, 256, 256) float16 FLUX.2 VAE posterior mean ‖ std of <N>.jpg |
| metadata/ |
| image_list_train.h5 datasets size_<size>: relative paths of every <N>.jpg |
| new_organ_split.csv organ ↔ (source, type) used in the paper |
| image_embedding_stats/uni2_{4096,8192}/normalization_stats.pt |
| per-position mean / var of UNI-2 grids over the full training set (used by the CGM) |
| ``` |
|
|
| | size | crops | used for | |
| |---|---|---| |
| | 2048 | 367 | training the tile generator (`_vae.npy` + `_uni.npy`) | |
| | 4096 / 8192 | 93 / 24 | CGM training grids; 4k / 8k generation | |
| | 16384 / 32768 | 7 / 2 | 16k / 32k generation | |
|
|
| ## Usage |
|
|
| ```bash |
| huggingface-cli download StonyBrook-CVLab/BigCell-sample-data --repo-type dataset --local-dir bigcell_sample_data |
| |
| # generate an 8192×8192 image from a real region's UNI-2 grid |
| python scripts/generate_from_grid.py --grid bigcell_sample_data/cptac/AML/C3L-00453-41_x0_y0/8192/0_uni.npy --out 8k.jpg |
| |
| # fine-tune the 2048 tile generator on the sample |
| torchrun --nproc_per_node 4 -m bigcell.train configs/bigcell/2048.yaml --data-dir bigcell_sample_data \ |
| --init-from bigcell-2048 --batch-size 8 --max-train-steps 100 |
| ``` |
|
|
| The TITAN report embeddings that the CGM is trained on are not part of this sample. |
|
|
| ## License |
|
|
| CC BY-NC-ND 4.0. The images derive from CPTAC and TCGA, which are subject to their original data-use terms. The |
| UNI-2h features and FLUX.2 latents are subject to the licences of those models. |
|
|
