Search Results for author: Ricardo Bigolin Lanfredi

Found 7 papers, 7 papers with code

Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification

1 code implementation6 Mar 2024 Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers

Additionally, using these improved annotations in classification supervision, we demonstrate substantial advancements in model quality, with an increase of 1. 7 pp in AUROC over models trained with annotations from the state-of-the-art approach.

Language Modelling Large Language Model +1

Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation

1 code implementation20 Jul 2022 Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Tolga Tasdizen

We extract snippets from the ET data by associating them with the dictation of keywords and use them to supervise the localization of specific abnormalities.

Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent

1 code implementation10 Sep 2020 Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Tolga Tasdizen

To evaluate the alignment with this direction after adversarial training, we apply a metric that uses generative adversarial networks to produce the smallest residual needed to change the class present in the image.

Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields

1 code implementation4 Jul 2020 Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet, Tolga Tasdizen

The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels.

Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays

1 code implementation27 Aug 2019 Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Clement Vachet, Tolga Tasdizen

We use a conditional generative adversarial network where the generator attempts to learn to shift the output of a regressor through creating disease effect maps that are added to the original images.

Generative Adversarial Network regression

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