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Crop-based dataset loaders for training on random 512x512 crops from native 1024x1024.
Both BCI and MIST variants share the same crop + augmentation logic.
UNI features are extracted on-the-fly on GPU (not pre-computed).
"""
import os
import random
from pathlib import Path
from typing import Optional, Tuple
import torch
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import torchvision.transforms as T
import torchvision.transforms.functional as TF
import pytorch_lightning as pl
class CropPairedDataset(Dataset):
"""Base class for random-crop paired H&E/IHC datasets.
Loads 1024x1024 images, takes a random 512x512 crop (same position for both),
and returns the crop + a UNI-ready version for on-the-fly feature extraction.
"""
def __init__(
self,
he_dir: str,
ihc_dir: str,
image_size: Tuple[int, int] = (512, 512),
crop_size: int = 512,
augment: bool = False,
labels: Optional[list] = None,
null_class: int = 4,
):
self.he_dir = Path(he_dir)
self.ihc_dir = Path(ihc_dir)
self.image_size = image_size
self.crop_size = crop_size
self.augment = augment
self.null_class = null_class
self.labels = labels
# UNI normalization (ImageNet stats, 224x224 per sub-crop)
self.uni_crop_transform = T.Compose([
T.Resize((224, 224), interpolation=T.InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
def _random_crop_pair(self, he_img, ihc_img):
"""Take the same random 512x512 crop from both images."""
w, h = he_img.size
if w < self.crop_size or h < self.crop_size:
raise ValueError(
f"Image size {w}x{h} smaller than crop size {self.crop_size}"
)
if w == self.crop_size and h == self.crop_size:
return he_img, ihc_img
left = random.randint(0, w - self.crop_size)
top = random.randint(0, h - self.crop_size)
he_crop = he_img.crop((left, top, left + self.crop_size, top + self.crop_size))
ihc_crop = ihc_img.crop((left, top, left + self.crop_size, top + self.crop_size))
return he_crop, ihc_crop
def _prepare_uni_sub_crops(self, he_pil):
"""Split 512x512 PIL crop into 4x4 sub-crops, each resized to 224x224 with UNI normalization.
Returns: [16, 3, 224, 224] tensor ready for UNI forward pass on GPU.
"""
w, h = he_pil.size
num_crops = 4
cw = w // num_crops
ch = h // num_crops
sub_crops = []
for i in range(num_crops):
for j in range(num_crops):
left = j * cw
top = i * ch
sub = he_pil.crop((left, top, left + cw, top + ch))
sub_crops.append(self.uni_crop_transform(sub))
return torch.stack(sub_crops) # [16, 3, 224, 224]
def _apply_paired_augmentations(self, he_img, ihc_img):
"""Apply identical spatial transforms to both images."""
if random.random() > 0.5:
he_img = TF.hflip(he_img)
ihc_img = TF.hflip(ihc_img)
if random.random() > 0.5:
he_img = TF.vflip(he_img)
ihc_img = TF.vflip(ihc_img)
if random.random() > 0.5:
k = random.choice([1, 2, 3])
he_img = TF.rotate(he_img, k * 90)
ihc_img = TF.rotate(ihc_img, k * 90)
if random.random() > 0.7:
angle = random.uniform(-15, 15)
translate = [random.uniform(-0.05, 0.05) * self.image_size[1],
random.uniform(-0.05, 0.05) * self.image_size[0]]
scale = random.uniform(0.9, 1.1)
he_img = TF.affine(he_img, angle, translate, scale, shear=0,
interpolation=T.InterpolationMode.BILINEAR)
ihc_img = TF.affine(ihc_img, angle, translate, scale, shear=0,
interpolation=T.InterpolationMode.BILINEAR)
return he_img, ihc_img
def _apply_he_color_augmentation(self, he_img):
"""Apply color jitter to H&E only (simulates staining variability)."""
if random.random() > 0.5:
he_img = TF.adjust_brightness(he_img, random.uniform(0.9, 1.1))
if random.random() > 0.5:
he_img = TF.adjust_contrast(he_img, random.uniform(0.9, 1.1))
if random.random() > 0.5:
he_img = TF.adjust_saturation(he_img, random.uniform(0.9, 1.1))
return he_img
def _process_pair(self, he_img, ihc_img, label, filename):
"""Common processing: crop -> augment -> tensorize -> UNI sub-crops.
Returns: (he_tensor, ihc_tensor, uni_sub_crops, label, filename)
- he_tensor: [3, 512, 512] in [-1, 1]
- ihc_tensor: [3, 512, 512] in [-1, 1]
- uni_sub_crops: [16, 3, 224, 224] with ImageNet normalization
"""
# Random crop (same position for both)
he_crop, ihc_crop = self._random_crop_pair(he_img, ihc_img)
# Augmentations (applied to PIL before UNI extraction, so features match)
if self.augment:
he_crop, ihc_crop = self._apply_paired_augmentations(he_crop, ihc_crop)
he_aug = self._apply_he_color_augmentation(he_crop)
else:
he_aug = he_crop
# Prepare UNI sub-crops from the augmented H&E crop
uni_sub_crops = self._prepare_uni_sub_crops(he_aug)
# Convert to training tensors [-1, 1]
he_tensor = TF.normalize(TF.to_tensor(he_aug), [0.5]*3, [0.5]*3)
ihc_tensor = TF.normalize(TF.to_tensor(ihc_crop), [0.5]*3, [0.5]*3)
return he_tensor, ihc_tensor, uni_sub_crops, label, filename
class BCICropDataset(CropPairedDataset):
"""BCI dataset with random 512 crops from 1024x1024 native images."""
HER2_LABEL_MAP = {'0': 0, '1+': 1, '2+': 2, '3+': 3}
def __init__(self, he_dir, ihc_dir, image_size=(512, 512),
crop_size=512, augment=False):
super().__init__(he_dir, ihc_dir, image_size, crop_size, augment)
self.he_images = sorted([f for f in os.listdir(he_dir) if f.endswith('.png')])
self.ihc_images = sorted([f for f in os.listdir(ihc_dir) if f.endswith('.png')])
assert len(self.he_images) == len(self.ihc_images)
self.labels = [self._parse_label(f) for f in self.he_images]
from collections import Counter
dist = Counter(self.labels)
print(f"BCI Crop Dataset: {len(self)} images, classes: {dict(sorted(dist.items()))}")
def _parse_label(self, filename):
parts = filename.replace('.png', '').split('_')
if len(parts) >= 3:
level = parts[2]
if level in self.HER2_LABEL_MAP:
return self.HER2_LABEL_MAP[level]
raise ValueError(f"Cannot parse label from: {filename}")
def __len__(self):
return len(self.he_images)
def __getitem__(self, idx):
filename = self.he_images[idx]
he_img = Image.open(self.he_dir / filename).convert('RGB')
ihc_img = Image.open(self.ihc_dir / self.ihc_images[idx]).convert('RGB')
return self._process_pair(he_img, ihc_img, self.labels[idx], filename)
class MISTCropDataset(CropPairedDataset):
"""MIST dataset with random 512 crops from 1024x1024 native images."""
def __init__(self, he_dir, ihc_dir, image_size=(512, 512),
crop_size=512, augment=False, null_class=4):
super().__init__(he_dir, ihc_dir, image_size, crop_size, augment,
null_class=null_class)
valid_exts = ('.jpg', '.jpeg', '.png')
self.he_images = sorted([f for f in os.listdir(he_dir)
if f.lower().endswith(valid_exts)])
self.ihc_images = sorted([f for f in os.listdir(ihc_dir)
if f.lower().endswith(valid_exts)])
# Verify pairing
he_stems = {Path(f).stem for f in self.he_images}
ihc_stems = {Path(f).stem for f in self.ihc_images}
if he_stems != ihc_stems:
common = he_stems & ihc_stems
self.he_images = sorted([f for f in self.he_images if Path(f).stem in common])
self.ihc_images = sorted([f for f in self.ihc_images if Path(f).stem in common])
print(f"Using {len(self.he_images)} matched pairs")
print(f"MIST Crop Dataset: {len(self)} images (null_class={null_class})")
def __len__(self):
return len(self.he_images)
def __getitem__(self, idx):
filename = self.he_images[idx]
he_img = Image.open(self.he_dir / filename).convert('RGB')
ihc_img = Image.open(self.ihc_dir / self.ihc_images[idx]).convert('RGB')
return self._process_pair(he_img, ihc_img, self.null_class, filename)
class BCICropDataModule(pl.LightningDataModule):
def __init__(self, data_dir, batch_size=4,
num_workers=4, image_size=(512, 512), crop_size=512):
super().__init__()
self.data_dir = Path(data_dir)
self.batch_size = batch_size
self.num_workers = num_workers
self.image_size = image_size
self.crop_size = crop_size
def setup(self, stage=None):
if stage == 'fit' or stage is None:
self.train_dataset = BCICropDataset(
he_dir=self.data_dir / 'HE' / 'train',
ihc_dir=self.data_dir / 'IHC' / 'train',
image_size=self.image_size,
crop_size=self.crop_size,
augment=True,
)
if stage in ('fit', 'validate', 'test') or stage is None:
self.val_dataset = BCICropDataset(
he_dir=self.data_dir / 'HE' / 'test',
ihc_dir=self.data_dir / 'IHC' / 'test',
image_size=self.image_size,
crop_size=self.crop_size,
augment=False,
)
def train_dataloader(self):
return DataLoader(
self.train_dataset, batch_size=self.batch_size, shuffle=True,
num_workers=self.num_workers, pin_memory=True,
persistent_workers=self.num_workers > 0,
)
def val_dataloader(self):
return DataLoader(
self.val_dataset, batch_size=self.batch_size, shuffle=False,
num_workers=self.num_workers, pin_memory=True,
persistent_workers=self.num_workers > 0,
)
def test_dataloader(self):
return self.val_dataloader()
class MISTCropDataModule(pl.LightningDataModule):
def __init__(self, data_dir, batch_size=4,
num_workers=4, image_size=(512, 512), crop_size=512, null_class=4):
super().__init__()
self.data_dir = Path(data_dir)
self.batch_size = batch_size
self.num_workers = num_workers
self.image_size = image_size
self.crop_size = crop_size
self.null_class = null_class
def setup(self, stage=None):
if stage == 'fit' or stage is None:
self.train_dataset = MISTCropDataset(
he_dir=self.data_dir / 'trainA',
ihc_dir=self.data_dir / 'trainB',
image_size=self.image_size,
crop_size=self.crop_size,
augment=True,
null_class=self.null_class,
)
if stage in ('fit', 'validate', 'test') or stage is None:
self.val_dataset = MISTCropDataset(
he_dir=self.data_dir / 'valA',
ihc_dir=self.data_dir / 'valB',
image_size=self.image_size,
crop_size=self.crop_size,
augment=False,
null_class=self.null_class,
)
def train_dataloader(self):
return DataLoader(
self.train_dataset, batch_size=self.batch_size, shuffle=True,
num_workers=self.num_workers, pin_memory=True,
persistent_workers=self.num_workers > 0,
)
def val_dataloader(self):
return DataLoader(
self.val_dataset, batch_size=self.batch_size, shuffle=False,
num_workers=self.num_workers, pin_memory=True,
persistent_workers=self.num_workers > 0,
)
def test_dataloader(self):
return self.val_dataloader()
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