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Found 6 papers, 3 papers with code
Tell2Reg: Establishing spatial correspondence between images by the same language prompts
Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displacement fields or transformation parameters.
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training.
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients.
Image quality assessment for machine learning tasks using meta-reinforcement learning
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability.
Adaptable image quality assessment using meta-reinforcement learning of task amenability
Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19. 7% and 29. 6% expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100% expert labels.
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation
Organ morphology is a key indicator for prostate disease diagnosis and prognosis.
