A U-Net with a ResNet-34 encoder pre-trained on ImageNet, fine-tuned end-to-end. Given a dermoscopy image the model produces a pixel-wise binary mask separating lesion from surrounding skin.

Encoder: ResNet-34, ImageNet pre-trained
Decoder: transposed-convolution upsampling with skip connections
Loss: 0.5 × BCEWithLogits + 0.5 × soft Dice
Parameters: 47.9M
Precision: mixed (torch.amp)

Evaluation Results

Held-out split of 390 images, scored one image at a time at threshold 0.5.

Metric	Score
Dice / F1	0.736
IoU (Jaccard)	0.620
Pixel accuracy	0.906

This is mid-range for the benchmark. Published ISIC 2018 baselines reach 0.85–0.90 Dice, and this model does not match them.

A note on how Dice was averaged

The training log for this run reports a best validation Dice of 0.772. That number pools every pixel in a batch of 16 into one overlap calculation, which lets large lesions dominate and dilutes errors on small ones. Averaging Dice per image, then taking the mean — the convention in the segmentation literature and on the ISIC leaderboard — gives 0.736 for the same checkpoint. The lower number is the one reported above and the one to compare against other work.

Pixel accuracy is included for completeness but is weak here: most dermoscopy images are mostly background, so predicting all-background already scores around 0.80.

Qualitative predictions

Each row: input image · ground truth · prediction with per-image Dice.

Per-image Dice across the six sampled cases: 0.930, 0.833, 0.797, 0.674, 0.593, 0.373. That spread is the honest picture. The best cases are large, well-demarcated pigmented lesions. The 0.373 case is a faint low-contrast lesion beside a red calibration sticker, which the model segments instead of the lesion.

Raw predictions also show speckle noise and spurious blobs on ruler markings and vignette borders. The demo app suppresses these at inference time by keeping only the largest connected component — ISIC ground truth is always a single contiguous lesion, so any additional region is a certain false positive.

Training Data

ISIC 2018 Challenge — Task 1: Lesion Segmentation

2,594 dermoscopy images with expert-annotated binary masks, covering diverse lesion types, sizes and acquisition conditions. Split 2,204 train / 390 validation with seed 42.

Training Details

Parameter	Value
Input size	256 × 256
Epochs	25 (early stopping patience 8, never triggered)
Batch size	16
Optimizer	AdamW, weight decay 1e-4
Learning rate	encoder 1e-4, decoder 1e-3
LR scheduler	Cosine annealing to 1e-6
Loss	BCE + soft Dice (0.5 / 0.5)
Precision	Mixed (`torch.amp`)
Train / val split	85% / 15%, seed 42
Hardware	Single Tesla T4, ~92 minutes

Augmentations: HorizontalFlip, VerticalFlip, RandomRotate90, Affine (translate ±5%, scale 0.9–1.1, rotate ±15°), ColorJitter, GaussNoise.

Differential learning rates

The encoder and decoder train at rates an order of magnitude apart. A single shared rate is the common reason a pre-trained-encoder U-Net stalls: a rate high enough to train a randomly initialised decoder washes ImageNet features out of the encoder, and a rate low enough to preserve them leaves the decoder barely moving.

Training curves

Train and validation loss track each other to the final epoch with no divergence, and validation Dice was still climbing at epoch 25. The model is underfitting, not overfitting. Added regularisation would not help; more capacity, higher input resolution, or a longer schedule would.

How to Use

pip install torch torchvision albumentations scipy huggingface_hub Pillow numpy

python

import numpy as np
import torch
import torch.nn.functional as F
import albumentations as A
from albumentations.pytorch import ToTensorV2
from PIL import Image
from huggingface_hub import hf_hub_download

from src.model import UNet          # copy model.py from the GitHub repo

ckpt_path = hf_hub_download(
    repo_id="NajmiHassan1/skinseg-vision",
    filename="best_weights.pth",
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet(pretrained=False).to(device)
ckpt = torch.load(ckpt_path, map_location=device, weights_only=True)
model.load_state_dict(ckpt["model_state"])
model.eval()

transform = A.Compose([
    A.Resize(256, 256),
    A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    ToTensorV2(),
])

image = np.array(Image.open("your_image.jpg").convert("RGB"))
h, w = image.shape[:2]
inp = transform(image=image)["image"].unsqueeze(0).to(device)

with torch.no_grad():
    probs = torch.sigmoid(model(inp))

# Threshold at full resolution, not at 256x256. Upsampling a binary mask
# with nearest-neighbour quantises the boundary to the 256-grid, which is a
# visible staircase on a 1022x767 dermoscopy image.
probs = F.interpolate(probs, size=(h, w), mode="bilinear", align_corners=False)
mask = (probs.squeeze().cpu().numpy() > 0.5).astype(np.uint8)

For test-time augmentation and mask cleanup — both worth using, both free — see src/inference.py in the GitHub repo.

Limitations

Dice 0.736 is below published baselines for this benchmark.
Weakest on low-contrast and amelanotic lesions, and on images containing rulers, ink markings or coloured stickers. In the sampled cases above, the worst failure segments a calibration sticker instead of the lesion.
Heavy vignetting degrades results; reliability drops near image borders.
Trained and evaluated at 256 × 256. Fine boundary detail is discarded at that resolution, which caps achievable Dice regardless of architecture.
Single train/validation split, no cross-validation, so the reported figure carries a meaningful error bar.
Dermoscopy only. Will not transfer to histology, clinical photography or other modalities.
Evaluated on ISIC 2018 alone; performance on other dermoscopy datasets is unmeasured.
Not a medical device. Not clinically validated.

Intended Use

Appropriate	Not appropriate
Research and experimentation	Clinical diagnosis
Benchmarking segmentation methods	Medical decision-making
Educational demonstration	Deployment without clinical validation
Pre-processing in research pipelines	Any safety-critical application

Citation

bibtex

@misc{hassan2026skinsegvision,
  author    = {Hassan, Najmi},
  title     = {Skin Lesion Segmentation with ResNet-34 U-Net on ISIC 2018},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/NajmiHassan1/skinseg-vision}
}

Dataset:

bibtex

@article{codella2019skin,
  title   = {Skin lesion analysis toward melanoma detection 2018: A challenge
             hosted by the International Skin Imaging Collaboration (ISIC)},
  author  = {Codella, Noel and Rotemberg, Veronica and Tschandl, Philipp and
             others},
  journal = {arXiv preprint arXiv:1902.03368},
  year    = {2019}
}

@article{tschandl2018ham10000,
  title   = {The HAM10000 dataset, a large collection of multi-source
             dermatoscopic images of common pigmented skin lesions},
  author  = {Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
  journal = {Scientific Data},
  volume  = {5},
  pages   = {180161},
  year    = {2018}
}

Acknowledgements

ISIC Archive for the dataset
torchvision for the ResNet-34 backbone
Albumentations for augmentation
Gradio for the demo interface