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Found 6 papers, 5 papers with code
FPT: Fine-grained Prompt Tuning for Parameter and Memory Efficient Fine Tuning in High-resolution Medical Image Classification
To achieve this, we first freeze the weights of the LPM and construct a learnable lightweight side network.
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability.
SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading
Specifically, two saliency-guided learning tasks are employed in SSiT: (1) Saliency-guided contrastive learning is conducted based on the momentum contrast, wherein fundus images' saliency maps are utilized to remove trivial patches from the input sequences of the momentum-updated key encoder.
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face Alignment
We innovatively propose a flexible and consistent cross-annotation face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way.
Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts.
