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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.
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## 2D Datasets
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The 2D datasets span a range of medical imaging modalities and classification tasks. They vary in complexity, from binary classification to multi-class problems, and are standardized to ensure consistent preprocessing. All images have dimensions of `(224, 224)` and pixel values normalized to the range `[0, 255]`.
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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.
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**Citation:**
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**Title:** Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation
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**Authors:** Mehmet Can Yavuz, Yang Yang
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**Year:** 2024
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**arXiv:** [arXiv:2411.02441](https://arxiv.org/abs/2411.02441)
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## 2D Datasets
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The 2D datasets span a range of medical imaging modalities and classification tasks. They vary in complexity, from binary classification to multi-class problems, and are standardized to ensure consistent preprocessing. All images have dimensions of `(224, 224)` and pixel values normalized to the range `[0, 255]`.
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