The field of medical imaging and diagnosis is rapidly evolving, with a focus on developing innovative and effective methods for disease detection and treatment. Recent studies have explored the use of deep learning techniques, such as contrastive learning and self-supervised learning, to improve the accuracy and efficiency of medical image analysis. These approaches have shown promising results in various applications, including glaucoma detection, retinal disease staging, and ultrasound image segmentation. Additionally, researchers have investigated the use of vision-language models and multimodal learning frameworks to enhance the interpretation of medical images and improve clinical decision-making. Overall, the field is moving towards more advanced and integrated approaches that combine multiple modalities and techniques to provide more accurate and comprehensive medical diagnoses. Noteworthy papers include RetFiner, which proposes a novel vision-language refinement scheme for retinal foundation models, and GroundingDINO-US-SAM, which integrates Grounding DINO with SAM2 for text-prompted multi-organ segmentation in ultrasound images. MR-CLIP is also notable for its multimodal contrastive learning framework that aligns MR images with their DICOM metadata to learn contrast-aware representations.
Current Trends in Medical Imaging and Diagnosis
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Early Glaucoma Detection using Deep Learning with Multiple Datasets of Fundus Images
RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models
Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization
Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound
Self-Supervised Contrastive Learning for Multi-Label Images
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
MReg: A Novel Regression Model with MoE-based Video Feature Mining for Mitral Regurgitation Diagnosis
Supercm: Revisiting Clustering for Semi-Supervised Learning
GroundingDINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models
MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations
Evaluating Large Language Models for Multimodal Simulated Ophthalmic Decision-Making in Diabetic Retinopathy and Glaucoma Screening
Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound
Wildlife Target Re-Identification Using Self-supervised Learning in Non-Urban Settings
Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms