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Found 10 papers, 8 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.
PRIOR: Prototype Representation Joint Learning from Medical Images and Reports
In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports.
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images.
UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment
Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications.
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.
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).
LesionPaste: One-Shot Anomaly Detection for Medical Images
Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
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.
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.
BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.
