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Found 8 papers, 0 papers with code
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.
Cluster Activation Mapping with Applications to Medical Imaging
An open question in deep clustering is how to understand what in the image is creating the cluster assignments.
Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Automatic pancreas segmentation in radiology images, eg., computed tomography (CT) and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment.
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection
Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis.
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner.
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process.
Revisiting Graph Construction for Fast Image Segmentation
In this paper, we propose a simple but effective method for fast image segmentation.
SemiContour: A Semi-supervised Learning Approach for Contour Detection
In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images).
