Determining the type of kidney stones is crucial for prescribing appropriate treatments to prevent recurrence. Currently, various approaches exist to identify the type of kidney stones. However, obtaining results through the reference ex vivo identification procedure can take several weeks, while in vivo visual recognition requires highly trained specialists. For this reason, deep learning models have been developed to provide urologists with an automated classification of kidney stones during ureteroscopies. Nevertheless, a common issue with these models is the lack of training data. This contribution presents a deep learning method based on few-shot learning, aimed at producing sufficiently discriminative features for identifying kidney stone types in endoscopic images, even with a very limited number of samples. This approach was specifically designed for scenarios where endoscopic images are scarce or where uncommon classes are present, enabling classification even with a limited training dataset. The results demonstrate that Prototypical Networks, using up to 25 % of the training data, can achieve performance equal to or better than traditional deep learning models trained with the complete dataset.

Contemporary Deep Learning (DL) image classifier approaches typically harness training set correlations to discern meaningful associations between inputs and outputs, often without differentiating causal connections from mere correlations. This practice can lead to Explainable Artificial Intelligence (XAI) techniques that, while identifying key input features, may base explanations on these correlations, thus risking confounded interpretations. This issue is particularly critical in medical imaging, where precise model explanations are vital. To tackle this, we build upon previous efforts for estimating causal links between model features and outputs, introducing the Explainable and Causal Feature Analysis (ECFA) method. Employing ECFA in a medical classification case study, we aim to empower medical professionals to differentiate between causally relevant model-extracted features and correlated features. Our experiments show that ECFA reliably pinpoints the top 1% of causal and anti-causal features to the output labels of a CNN-based classifier, aiding in the assessment of whether model predictions are causally grounded or correlation-based. This facilitates a more informed evaluation of whether a model’s predictions derive from distinguishable causal links or not, marking a notable stride toward enhancing the reliability and interpretability of DL models in medical diagnostics.

Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR). Both methods rely on features observed in the surface and the section of kidney stones to separate the analyzed samples into several sub-groups. However, given the high intra-observer variability and the complex operating conditions found in ESR, there is a lot of interest in using AI for computer-aided diagnosis. However, current AI models require large datasets to attain a good performance and for generalizing to unseen distributions. This is a major problem as large labeled datasets are very difficult to acquire, and some classes of kidney stones are very rare. Thus, in this paper, we present a method based on diffusion as a way of augmenting pre-existing ex-vivo kidney stone datasets. Our aim is to create plausible diverse kidney stone images that can be used for pre-training models using ex-vivo data. We show that by mixing natural and synthetic images of CCD images, it is possible to train models capable of performing very well on unseen intra-operative data. Our results show that is possible to attain an improvement of 10% in terms of accuracy compared to a baseline model pre-trained only on ImageNet. Moreover, our results show an improvement of 6% for surface images and 10% for section images compared to a model train on CCD images only, which demonstrates the effectiveness of using synthetic images.

The study of brain vascular patterns in preterm infants is relevant for identifying pathologies associated with brain irrigation. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficulties in the quantification of the vessel patterns. The goal of this research is to enhance the images for a subsequent segmentation stage. Thus, as a result of this research, an entire pipeline of denoising and segmentation is presented as a solution. For denoising the images, the combination of conventional techniques with unsupervised techniques based on deep learning was explored. The best method for the removal of noise was the combination of traditional methods and PN2V using a GMM model. A UNet model was trained utilizing noisy pictures for segmentation. Then it was tested using both denoised and noisy images. The findings demonstrated an improvement of 9.4% in the dice score when the model was trained using noisy images.