The field of medical image analysis is witnessing significant developments with the integration of deep learning techniques and cross-modal learning approaches. Researchers are exploring innovative methods to improve the accuracy and efficiency of medical image analysis, such as the use of contrastive learning frameworks and hybrid architectures combining convolutional neural networks and transformers. These advancements have the potential to enhance disease diagnosis, patient outcomes, and clinical decision-making. Notable papers in this area include those proposing novel frameworks for cross-modal alignment of medical images and texts, such as EEG-CLIP and SeLIP, which demonstrate promising results in zero-shot decoding and image-text retrieval tasks. Additionally, papers like AutoRad-Lung and NeuroLIP introduce innovative approaches to lung nodule malignancy prediction and cross-modal alignment of fMRI and phenotypic text, respectively, with a focus on interpretability and fairness.
Advances in Medical Image Analysis and Cross-Modal Learning
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EEG-CLIP : Learning EEG representations from natural language descriptions
MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification
A-IDE : Agent-Integrated Denoising Experts
MEPNet: Medical Entity-balanced Prompting Network for Brain CT Report Generation
Image-to-Text for Medical Reports Using Adaptive Co-Attention and Triple-LSTM Module
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI
iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species
Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction
Retinal Fundus Multi-Disease Image Classification using Hybrid CNN-Transformer-Ensemble Architectures
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text