Kvasir Unet

Input: (B, 3, 256, 256) RGB images → Output: (B, 1, 256, 256) segmentation logits

Loss Function

Combined BCE + Dice Loss to address class imbalance:

• BCE (Binary Cross-Entropy): Handles pixel-level binary classification
• Dice Loss: Directly optimizes the overlap between predicted and ground-truth masks, counteracting the dominance of background pixels in medical images

Dataset

Trained on Kvasir-SEG — gastrointestinal polyp segmentation dataset.

References

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI 2015.

--- tags: - pytorch - image-segmentation - unet - medical-imaging datasets: - Angelou0516/kvasir-seg --- # UNet for Kvasir-SEG Polyp Segmentation ## Architecture Standard U-Net architecture (Ronneberger et al., 2015) for binary medical image segmentation. - **Encoder**: 4 downsampling stages (3→64→128→256→512) with double convolution blocks and max pooling - **Bottleneck**: 512→1024 channels at the lowest spatial resolution - **Decoder**: 4 upsampling stages using transposed convolutions with skip connections from the encoder - **Output**: 1×1 convolution producing a single-channel binary segmentation mask Input: `(B, 3, 256, 256)` RGB images → Output: `(B, 1, 256, 256)` segmentation logits ## Loss Function **Combined BCE + Dice Loss** to address class imbalance: - **BCE (Binary Cross-Entropy)**: Handles pixel-level binary classification - **Dice Loss**: Directly optimizes the overlap between predicted and ground-truth masks, counteracting the dominance of background pixels in medical images ## Dataset Trained on [Kvasir-SEG](https://huggingface.co/datasets/Angelou0516/kvasir-seg) — gastrointestinal polyp segmentation dataset. ## References Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI 2015.