brain-mri-segmentation

Production-grade binary brain-tumor MRI segmentation (LGG / TCGA).

Metrics

Metric	Value
dice	0.6549053192138672
iou	0.6620140075683594
pixel_accuracy	0.997348964214325
test_size	387

Usage

from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
import torch
from PIL import Image

processor = AutoImageProcessor.from_pretrained("kiselyovd/brain-mri-segmentation")
model = AutoModelForSemanticSegmentation.from_pretrained("kiselyovd/brain-mri-segmentation")

image = Image.open("your_image.png")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
logits = outputs.logits  # shape (batch_size, num_labels, height, width)

Training Data

Trained on LGG MRI Segmentation (TCGA).

Source Code

GitHub Repository

Intended Use

This model is provided for research and educational purposes. The authors make no warranties about its suitability for any particular application. Users are responsible for evaluating the model's fitness for their use case, including fairness, safety, and compliance with applicable regulations.

Note: This model card was generated from the ml-project-template scaffold.