Update README.md

README.md

@@ -10,7 +10,7 @@ This is the official repository for the implementation of (paper link)
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-  <em>Fig-1: An overview of the proposed framework. (a) Self-supervised pre-training using diffusion: The U-Net model (encoder-decoder) takes the corrupted version https://latex.codecogs.com/svg.latex?\mathbf{x}_t  of the image $\mathbf{x}_0$ and the corresponding time embedding $t_e$ as the input to predict the noise that takes $\mathbf{x}_0$ to $\mathbf{x}_t$, using the P2 weighted loss. $f(\cdot)$ denotes the function that recovers $\mathbf{x}_{t-1}$ from $\mathbf{x}_t$. (b) Downstream segmentation: The self-supervised pre-trained U-Net is fine-tuned end-to-end in a supervised manner to predict the segmentation masks.</em>
+  <em>Fig-1: An overview of the proposed framework. (a) Self-supervised pre-training using diffusion: The U-Net model (encoder-decoder) takes the corrupted version ![x_t](https://latex.codecogs.com/png.latex?\mathbf{x}_t)  of the image $\mathbf{x}_0$ and the corresponding time embedding $t_e$ as the input to predict the noise that takes $\mathbf{x}_0$ to $\mathbf{x}_t$, using the P2 weighted loss. $f(\cdot)$ denotes the function that recovers $\mathbf{x}_{t-1}$ from $\mathbf{x}_t$. (b) Downstream segmentation: The self-supervised pre-trained U-Net is fine-tuned end-to-end in a supervised manner to predict the segmentation masks.</em>
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 # Pre-process