MetPredict Lung Structure Segmentation

--- library_name: transformers tags: - image-segmentation - pathology - dpt pipeline_tag: image-segmentation --- # Lung structures Segmentation (DPT) Pathology segmentation for lung structures (blood vessels and airways). - Encoder (freezed): H-optimus-0 ViT backbone (pretrained on histopathology data). - Decoder (trained): custom DPT head with multi-scale feature fusion. ## Usage The model expects a normalized `(B, 3, H, W)` float tensor as `pixel_values`. Use ImageNet mean/std — same stats applied at training time (matches the H-optimus-0 backbone's expected input distribution). Input image: 224x224 @ 1.5 MPP ```python import numpy as np import torch from PIL import Image from torchvision.transforms import ToTensor, Normalize, Resize, Compose from transformers import AutoModel model = AutoModel.from_pretrained("RendeiroLab/MetPredict-lung-structure-segmentation", trust_remote_code=True).eval() device = next(model.parameters()).device transform = Compose([ ToTensor(), Resize((224, 224)), Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), ]) img = Image.open("tile.png").convert("RGB") x = transform(img) pixel_values = x.unsqueeze(0).to(device) with torch.inference_mode(): out = model(pixel_values) logits = out.logits # (1, n_classes, H, W) pred = logits.argmax(dim=1) # (1, H, W) ```

Source context: 7 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo RendeiroLab/MetPredict-lung-structure-segmentation

Source context: 4 downloads · 0 likes · Pipeline image-segmentation · Library transformers · Repo RendeiroLab/MetPredict-lung-structure-segmentation