Fix conv1.5 article link

README.md

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 ## Model Summary
 
-**HistAug** is a lightweight transformer-based generator for **controllable latent-space augmentations** in the feature space of the [CONCH v1.5 foundation model](https://arxiv.org/abs/2408.00738). Instead of applying costly image-space augmentations on millions of WSI patches, HistAug operates **directly on patch embeddings** extracted from a given foundation model(here CONCH v1.5). By conditioning on explicit transformation parameters (e.g., hue shift, erosion, HED color transform), HistAug generates realistic augmented embeddings while preserving semantic content. In practice, the CONCH v1.5 variant of HistAug can reconstruct the corresponding ground-truth augmented embeddings with an average cosine similarity of **about 92%** at **10X, 20X, and 40X magnification**.
+**HistAug** is a lightweight transformer-based generator for **controllable latent-space augmentations** in the feature space of the [CONCH v1.5 foundation model](https://arxiv.org/abs/2411.19666). Instead of applying costly image-space augmentations on millions of WSI patches, HistAug operates **directly on patch embeddings** extracted from a given foundation model(here CONCH v1.5). By conditioning on explicit transformation parameters (e.g., hue shift, erosion, HED color transform), HistAug generates realistic augmented embeddings while preserving semantic content. In practice, the CONCH v1.5 variant of HistAug can reconstruct the corresponding ground-truth augmented embeddings with an average cosine similarity of **about 92%** at **10X, 20X, and 40X magnification**.
 
 This enables training of Multiple Instance Learning (MIL) models with:  
 - ⚡ **Fast augmentation**