TL;DR
The results show that larger models and larger pretraining datasets do not automatically lead to better downstream performance, and transfer effectiveness in medical imaging is driven primarily by architectural inductive biases, pretraining strategy, and domain relevance.
TransCat, a hybrid CNN-Transformer architecture for medical image segmentation, is proposed and an extended deformable attention mechanism with attentive value identification is developed, to control the computational burden caused by the enlarged token set.
A comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures is presented, showing that deeper networks provide better representation, while residual connections and compound scaling improve training stab...
The proposed framework is intended to support clinical image assessment and prioritization rather than replace expert diagnosis, and demonstrates the potential of hybrid CNN-Transformer architectures for robust and scalable computer-assisted multi-disease screening from medical imaging data.
This study systematically compares representative standard and lightweight CNN- and transformer-based architectures for multi-class DR grading and offers practical guidance for selecting suitable deep learning models for automated diabetic retinopathy screening.
The proposed HrybridViT-CAM, a hybrid deep learning system that integrates convolution neural networks, Vision Transformers, and multi-scale attention system in order to classify breast cancer using histopathology images was able to detect the malignant regions of interest (ROIs) like nuclei pleomorphism, atypia chroma...
A pretrained lightweight Conformer model tailored for medical image classification that integrates convolutional layers for capturing fine-grained spatial features with transformer blocks that capture long-range dependencies, creating a unified architecture capable of robust representation learning.
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