7 research outputs found
Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data
Retinal image matching plays a crucial role in monitoring disease progression
and treatment response. However, datasets with matched keypoints between
temporally separated pairs of images are not available in abundance to train
transformer-based model. We propose a novel approach based on reverse knowledge
distillation to train large models with limited data while preventing
overfitting. Firstly, we propose architectural modifications to a CNN-based
semi-supervised method called SuperRetina that help us improve its results on a
publicly available dataset. Then, we train a computationally heavier model
based on a vision transformer encoder using the lighter CNN-based model, which
is counter-intuitive in the field knowledge-distillation research where
training lighter models based on heavier ones is the norm. Surprisingly, such
reverse knowledge distillation improves generalization even further. Our
experiments suggest that high-dimensional fitting in representation space may
prevent overfitting unlike training directly to match the final output. We also
provide a public dataset with annotations for retinal image keypoint detection
and matching to help the research community develop algorithms for retinal
image applications
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications
Graph neural networks (GNNs) present a promising alternative to CNNs and
transformers in certain image processing applications due to their
parameter-efficiency in modeling spatial relationships. Currently, a major area
of research involves the converting non-graph input data for GNN-based models,
notably in scenarios where the data originates from images. One approach
involves converting images into nodes by identifying significant keypoints
within them. Super-Retina, a semi-supervised technique, has been utilized for
detecting keypoints in retinal images. However, its limitations lie in the
dependency on a small initial set of ground truth keypoints, which is
progressively expanded to detect more keypoints. Having encountered
difficulties in detecting consistent initial keypoints in brain images using
SIFT and LoFTR, we proposed a new approach: radiomic feature-based keypoint
detection. Demonstrating the anatomical significance of the detected keypoints
was achieved by showcasing their efficacy in improving registration processes
guided by these keypoints. Subsequently, these keypoints were employed as the
ground truth for the keypoint detection method (LK-SuperRetina). Furthermore,
the study showcases the application of GNNs in image matching, highlighting
their superior performance in terms of both the number of good matches and
confidence scores. This research sets the stage for expanding GNN applications
into various other applications, including but not limited to image
classification, segmentation, and registration