Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train. Due to data privacy concerns and unavailability of medical annotators, it is oftentimes very difficult to obtain a lot of labeled medical images for model training. In this paper, we study cross-modality data augmentation to mitigate the data deficiency issue in the medical imaging domain. We propose a discriminative unpaired image-to-image translation model which translates images in source modality into images in target modality where the translation task is conducted jointly with the downstream prediction task and the translation is guided by the prediction. Experiments on two applications demonstrate the effectiveness of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationImage-to-Image TranslationMedical Image AnalysisTranslationSimilar Papers 제목 키워드 기반
BioVLM: Routing Prompts, Not Parameters, for Cross-Modality Generalization in Biomedical VLMs
Pretrained biomedical vision-language models (VLMs) such as BioMedCLIP perform well on average but often degrade on challenging modalities where inter-class margins are small and acquisition-specific variations are prono…
Domain GeneralizationDiscriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes
Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurate results. In efforts to overcome this c…
Data AugmentationImage GenerationImage SegmentationMedical Image Segmentation+2Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting
In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Adversarial Networks (GANs) are increasing…
Data AugmentationDiversitySemi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
Medical referring image segmentation (MRIS) predicts lesion masks from medical images and natural-language referring expressions, but acquiring paired pixel-level annotations and referring texts is costly. Semi-supervise…
Contrastive LearningImage SegmentationContrastive Domain Disentanglement for Generalizable Medical Image Segmentation
Efficiently utilizing discriminative features is crucial for convolutional neural networks to achieve remarkable performance in medical image segmentation and is also important for model generalization across multiple do…
DisentanglementDomain GeneralizationImage SegmentationMedical Image Segmentation+2