Abstract
This paper is concerned with the semantic segmentation within domain-specific contexts, such as those pertaining to biology, physics, or material science. Under these circumstances, the objects of interest are often irregular and have fine structure, i.e., detail at arbitrarily small scales. Empirically, they are often understood as self-similar processes, a concept grounded in Multifractal Analysis. We find that this multifractal behaviour is carried out through a convolutional neural network (CNN), if we view its channel-wise responses as self-similar measures. A function of the local singularities of each measure we call Singularity Stregth Recalibration (SSR) is set forth to modulate the response at each layer of the CNN. SSR is a lightweight, plug-in module for CNNs. We observe that it improves a baseline U-Net in two biomedical tasks: skin lesion and colonic polyp segmentation, by an average of 1.36% and 1.12% Dice score, respectively. To the best of our knowledge, this is the first time multifractal-analysis is conducted end-to-end for semantic segmentation.

Abstract
ABSTRACTCrowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We focus on a binary (natural, human) and a multiclass (posing, species, nature, landscape, human activities, human structures) classification of those elements using two Convolutional Neural Networks (CNNs; VGG16 and ResNet152) with the weights from two large datasets - Places365 and ImageNet -, and our own dataset. We train those CNNs over Flickr and Wikiloc images from the Peneda-Gerês region (Portugal) and evaluate their transferability to wider areas, using Sierra Nevada (Spain) as test. CNNs trained for Peneda-Gerês performed well, with results for the binary classification (F1-score > 80%) exceeding those for the multiclass classification (> 60%). CNNs pre-trained with Places365 and ImageNet data performed significantly better than with our data. Model performance decreased when transferred to Sierra Nevada, but their performances were satisfactory (> 60%). The combination of manual annotations, freely available CNNs and pre-trained local datasets thereby show great relevance to support automated CES assessments from social media.

Abstract
Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy. The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted. Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18). The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.Abstract Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy. The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted. Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18). The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.Abstract Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy. The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted. Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18). The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.Abstract Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy. The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted. Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18). The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.

Abstract
We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the performance of five distinct models for binary classification (normal/abnormal) of synchronized heart sounds and electrocardiograms. The applied models comprise a one-dimensional convolutional neural network (1D-CNN) using solely ECG signals, a two-dimensional convolutional neural network (2D-CNN) applied separately to PCG and ECG signals, and two multimodal models that employ both signals. In the multimodal models, we implement two fusion approaches: an early fusion and a late fusion. The results indicate a performance improvement in using an early fusion model for the joint classification of both signals, as opposed to using a PCG 2D-CNN or ECG 1D-CNN alone (e.g., ROC-AUC score of 0.81 vs. 0.79 and 0.79, respectively). Although the ECG 2D-CNN demonstrates a higher ROC-AUC score (0.82) compared to the early fusion model, it exhibits a lower F1-score (0.85 vs. 0.86). Grad-CAM unveils that the models tend to yield higher gradients in the QRS complex and T/P-wave of the ECG signal, as well as between the two PCG fundamental sounds (S1 and S2), for discerning normalcy or abnormality, thus showcasing that the models focus on clinically relevant features of the recorded data.

Abstract
In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of gastritis atrophy (GA) and intestinal metaplasia (IM) conditions. GA and IM are precancerous gastric lesions. Detecting these lesions is crucial to intervene early and prevent their progression to cancer. A set of experiments is conducted over the Chengdu dataset, by considering different amounts of annotated data in the training phase. Our results reveal that, when all available data is used for training, SSL approaches achieve a classification accuracy on par with a supervised learning baseline, (81.52% vs 81.76%). Interestingly, we observe that in low-data regimes (here represented as retaining only 12.5% of annotated data for training), the SSL model guarantees an accuracy gain with respect to the supervised learning baseline of approximately 1.5% (73.00% vs 71.52%). This observation hints at the potential of SSL models in leveraging unlabeled data, thus showcasing more robust performance improvements and generalization. Experimental results also show that SSL performance is significantly dependent on the specific data augmentation techniques and parameters adopted for contrastive learning, thus advocating for further investigations into the definition of optimal data augmentation frameworks specifically tailored for gastric lesion detection applications.

Abstract
Early diagnosis of cardiovascular diseases is essential for an effective treatment, potentially preventing severe health complications and improving clinical outcomes. Electrocardiogram (ECG) and phonocardiogram (PCG) are cost-effective, noninvasive diagnostic tools providing crucial and complementary information about the heart's electrical and mechanical activities. This paper presents a novel approach to the assessment of cardiovascular health through the multimodal analysis of simultaneously recorded ECG and PCG signals. Combining multimodal analysis and transfer learning on publicly available data, the most successful multimodal approach achieved an accuracy of 82.79%, a ROC AUC score of 91.26%, and a recall of 93.10% demonstrating the potential of these techniques. This study provides a foundation for future research aimed at enhancing the performance of multimodal cardiac abnormality detection systems. © 2024 IEEE.