Exploiting XAI maps to improve MS lesion segmentation and detection in MRI

• URL: http://arxiv.org/abs/2409.03772v1
• Date: Wed, 21 Aug 2024 07:49:01 GMT
• Title: Exploiting XAI maps to improve MS lesion segmentation and detection in MRI
• Authors: Federico Spagnolo, Nataliia Molchanova, Mario Ocampo Pineda, Lester Melie-Garcia, Meritxell Bach Cuadra, Cristina Granziera, Vincent Andrearczyk, Adrien Depeursinge,
• Abstract summary: We explore the use of characteristics of lesion-specific saliency maps to refine segmentation and detection scores. 93 radiomic features extracted from the first set of maps were used to train a logistic regression model. On the test set, F1 score and PPV were improved by a large margin when compared to the initial model.
• Score: 1.024819169163989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To date, several methods have been developed to explain deep learning algorithms for classification tasks. Recently, an adaptation of two of such methods has been proposed to generate instance-level explainable maps in a semantic segmentation scenario, such as multiple sclerosis (MS) lesion segmentation. In the mentioned work, a 3D U-Net was trained and tested for MS lesion segmentation, yielding an F1 score of 0.7006, and a positive predictive value (PPV) of 0.6265. The distribution of values in explainable maps exposed some differences between maps of true and false positive (TP/FP) examples. Inspired by those results, we explore in this paper the use of characteristics of lesion-specific saliency maps to refine segmentation and detection scores. We generate around 21000 maps from as many TP/FP lesions in a batch of 72 patients (training set) and 4868 from the 37 patients in the test set. 93 radiomic features extracted from the first set of maps were used to train a logistic regression model and classify TP versus FP. On the test set, F1 score and PPV were improved by a large margin when compared to the initial model, reaching 0.7450 and 0.7817, with 95% confidence intervals of [0.7358, 0.7547] and [0.7679, 0.7962], respectively. These results suggest that saliency maps can be used to refine prediction scores, boosting a model's performances.

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