- Read more about STABLE OPTIMIZATION FOR LARGE VISION MODEL BASED DEEP IMAGE PRIOR IN CONE-BEAM CT RECONSTRUCTION
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Large Vision Model (LVM) has recently demonstrated great potential for medical imaging tasks, potentially enabling image enhancement for sparse-view Cone-Beam Computed Tomography (CBCT), despite requiring a substantial amount of data for training. Meanwhile, Deep Image Prior (DIP) effectively guides an untrained neural network to generate high-quality CBCT images without any training data. How- ever, the original DIP method relies on a well-defined forward model and a large-capacity backbone network, which is no- toriously difficult to converge.
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- Read more about BLIND INPAINTING WITH OBJECT-AWARE DISCRIMINATION FOR ARTIFICIAL MARKER REMOVAL
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Medical images often incorporate doctor-added markers that can hinder AI-based diagnosis. This issue highlights the need of inpainting techniques to restore the corrupted visual contents. However, existing methods require manual mask annotation as input, limiting the application scenarios. In this paper, we propose a novel blind inpainting method that automatically reconstructs visual contents within the corrupted regions without mask input as guidance. Our model includes a blind reconstruction network and an object-aware discriminator for adversarial training.
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- Read more about Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation
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Leveraging pre-trained visual language models has become a widely adopted approach for improving performance in downstream visual question answering (VQA) applications. However, in the specialized field of medical VQA, the scarcity of available data poses a significant barrier to achieving reliable model generalization. Numerous methods have been proposed to enhance model generalization, addressing the issue from data-centric and model-centric perspectives.
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- Read more about Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration
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We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard. The framework we design includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration module to align images.
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- Read more about AN AUTOMATIC COLORECTAL POLYPS DETECTION APPROACH FOR CT COLONOGRAPHY
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- Read more about SEMI-SUPERVISED CONTRASTIVE LEARNING OF GLOBAL AND LOCAL REPRESENTATION FOR 3D MEDICAL IMAGE SEGMENTATION
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- Read more about Ensemble Methods for Enhanced COVID-19 CT scan severity analysis
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Computed Tomography (CT) scans provide a high-resolutionimage of the lungs, allowing clinicians to identify the severity of infections in COVID-19 patients. This paper presents a domain knowledge-based pipeline for extracting infection regions from COVID-19 patients using a combination of image processing algorithms and a pre-trained UNET model. Then, an infection rate-based feature vector is generated for each CT scan.
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- Read more about IDEAL: Improved DEnse LocAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
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Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task.
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- Read more about IDEAL: Improved DEnse LocAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
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Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task.
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- Read more about SELF-KNOWLEDGE DISTILLATION BASED SELF-SUPERVISED LEARNING FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGES
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1206-3.pdf
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