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Found 9 papers, 5 papers with code
Parameter choices in HaarPSI for IQA with medical images
We observe that they are more sensitive to the parameter choices than the employed natural images, and on the other hand both medical data sets lead to similar parameter values when optimized.
Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology
Developing imaging models capable of detecting pathologies from chest X-rays can be cost and time-prohibitive for large datasets as it requires supervision to attain state-of-the-art performance.
A study on the adequacy of common IQA measures for medical images
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images.
A study of why we need to reassess full reference image quality assessment with medical images
Image quality assessment (IQA) is not just indispensable in clinical practice to ensure high standards, but also in the development stage of novel algorithms that operate on medical images with reference data.
visClust: A visual clustering algorithm based on orthogonal projections
The code is made available on GitHub and straightforward to use.
Blood vessel segmentation in en-face OCTA images: a frequency based method
The results underline the visual evaluation.
An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions.
Improving Machine Hearing on Limited Data Sets
In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting.
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy.
