Update README.md

README.md

@@ -23,11 +23,15 @@ datasets:
 Transfer learning in machine learning models, particularly deep learning architectures, requires diverse datasets to ensure robustness and generalizability across tasks and domains. This repository provides comprehensive details on the datasets used for evaluation, categorized into **2D** and **3D datasets**. These datasets span variations in image dimensions, pixel ranges, label types, and unique labels, facilitating a thorough assessment of fine-tuning capabilities.
 
 **Citation:**
-
-**Title:** Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation  
-**Authors:** Mehmet Can Yavuz, Yang Yang  
-**Year:** 2024  
-**arXiv:** [arXiv:2411.02441](https://arxiv.org/abs/2411.02441)
+```
+@online{2411.02441,
+Author = {Mehmet Can Yavuz and Yang Yang},
+Title = {Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation},
+Year = {2024},
+Eprint = {2411.02441},
+Eprinttype = {arXiv},
+}
+```
 
 ## 2D Datasets
 
@@ -102,16 +106,6 @@ These datasets collectively provide:
 
 These datasets are curated to facilitate robust and generalizable machine learning models for real-world medical applications.
 
-```
-@online{2411.02441,
-Author = {Mehmet Can Yavuz and Yang Yang},
-Title = {Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation},
-Year = {2024},
-Eprint = {2411.02441},
-Eprinttype = {arXiv},
-}
-```
-
 ---
 
 1. Acevedo et al. (2020)