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Found 7 papers, 6 papers with code
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
To adapt SAM to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available.
Do Vision and Language Encoders Represent the World Similarly?
In the absence of statistical similarity in aligned encoders like CLIP, we show that a possible matching of unaligned encoders exists without any training.
Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts
Meanwhile, recent developments in generative pretrained models like GPT-4 mean they can be used as advanced internet search tools.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans
Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality.
BaseTransformers: Attention over base data-points for One Shot Learning
In this paper we propose to make use of the well-trained feature representations of the base dataset that are closest to each support instance to improve its representation during meta-test time.
Phase retrieval for Fourier Ptychography under varying amount of measurements
For the low overlap case we show that a supervised deep learning technique using an autoencoder generator is a good choice for solving the Fourier ptychography problem.
