Predicting brain age from structural Magnetic Resonance Imaging (MRI) has emerged as a critical task at the intersection of medical imaging and Artificial Intelligence, with Deep learning (DL) models achieving state-of-the-art performance. However, despite their predictive power, such models remain susceptible to algorithmic bias, especially when applied to populations whose demographic characteristics differ from those seen during training. In this paper, we investigate how demographic factors influence the performance of brain age prediction models. We leverage a large, demographically diverse MRI dataset including 7480 healthy subjects (3599 female and 3881 male) spanning three major racial groups: White, Black, and Asian. To explore the effects of data composition and model architecture on generalization, we design and compare multiple training paradigms, including models trained on single group and a Multi-Input architecture that explicitly incorporates demographic metadata. Results on an external test set including 3194 subjects (2162 White, 694 Black, and 338 Asian) reveal evidence of demographic bias, with the Multi-Input model achieving the most balanced performance across groups (mean absolute error: 2.94 ± 0.07 for White, 2.91 ± 0.16 for Black, and 3.34 ± 0.17 for Asian subjects). These findings highlight the need for fairness-aware approaches, advocating for strategies that mitigate bias, and enhance generalizability. • 3D CNNs for brain age prediction in structural MRI scans. • Demographic bias assessment across White, Black, and Asian subgroups. • Comparison of four training paradigms (TPs) in fairness and generalization.

Alzheimer's Disease is the most common cause of dementia, whose progression spans in different stages, from very mild cognitive impairment to mild and severe conditions. In clinical trials, Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are mostly used for the early diagnosis of neurodegenerative disorders since they provide volumetric and metabolic function information of the brain, respectively. In recent years, Deep Learning (DL) has been employed in medical imaging with promising results. Moreover, the use of the deep neural networks, especially Convolutional Neural Networks (CNNs), has also enabled the development of DL-based solutions in domains characterized by the need of leveraging information coming from multiple data sources, raising the Multimodal Deep Learning (MDL). In this paper, we conduct a systematic analysis of MDL approaches for dementia severity assessment exploiting MRI and PET scans. We propose a Multi Input-Multi Output 3D CNN whose training iterations change according to the characteristic of the input as it is able to handle incomplete acquisitions, in which one image modality is missed. Experiments performed on OASIS-3 dataset show the satisfactory results of the implemented network, which outperforms approaches exploiting both single image modality and different MDL fusion techniques.

In biomedical image processing, Deep Learning (DL) is increasingly exploited in various forms and for diverse purposes. Despite unprecedented results, the huge number of parameters to learn, which necessitates a substantial number of annotated samples, remains a significant challenge. In medical domains, obtaining high-quality labelled datasets is still a challenging task. In recent years, several works have leveraged data augmentation to face this issue, mostly thanks to the introduction of generative models able to produce artificial samples having the same characteristics as the acquired ones. However, we claim that biological principles must be considered in this process, as all medical imaging techniques exploit one or more physical laws or properties directly associated with the physiological characteristics of the tissues under analysis. A notable example is the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the contrast agent (CA) highlights both morphological and physiological aspects. In this paper, we introduce a novel generative approach explicitly relying on Physiologically Based Pharmacokinetic (PBPK) modelling and on an Intrinsic Deforming Autoencoder (DAE) to implement a physiologically-aware data augmentation strategy. As a case of study, we consider breast DCE-MRI. In particular, we tested our proposal on two private and one public datasets with different acquisition protocols, demonstrating that the proposed method significantly improves the performance of several DL-based lesion classifiers.

The involvement of axillary lymph node metastasis in breast cancer is one of the most important independent prognostic factors. While the metastasis of lymph node depends on primary tumour intrinsic behaviour, morphology and angioinvasivity, the involvement of the peritumoral tissue by the neoplastic cells also provides useful information for the potential tumour aggressiveness. The lymph node status is currently evaluated by histological invasive procedures with possible complications, asking for introducing safer approaches. Among different imaging techniques, the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) highlights physiological and morphological characteristics, reflecting breast lesions behaviour and aggressiveness. In the recent years, deep learning (DL) approaches, such as Convolutional Neural Networks, gained increasing popularity for biomedical image processing. Thanks to their ability to autonomously learn from images the set of features for the specific task to solve, they allow finding non-invasive alternatives to the standard procedures used up to now. This paper aims to evaluate the applicability of DL approaches for the axillary lymph node metastasis prediction, considering primary tumour DCE-MRI sequence. Differently from other work in the literature, we include a detailed analysis of healthy tissue influence in lymph node tumour spread through the evaluation of different tumour bounding options. Promising results are reported on a dataset of 153 patients with 155 malignant lesions.

Hard disk drive failures are one of the most common causes of service downtime in data centers. Predictive maintenance techniques have been adopted to extend the Remaining Useful Life (RUL) of these drives, and minimize service shortage and data loss. Several approaches based on machine and deep learning techniques have been proposed to address these issues, mostly exploiting models based on Self-Monitoring analysis and Reporting Technology (SMART) attributes. While these models have proven to be reliable, their performance is affected by the lack of information about the proximity of disk failure in time. Moreover, many of these techniques are sensitive to the highly unbalanced nature of existing data-sets, in terms of good to failed hard disk ratio. In this article, we propose a LSTM (Long Short Term Memory)-based model combining SMART attributes and temporal analysis for estimating a hard drive health status according to its time to failure. Our approach outperforms state-of-the-art methods when evaluated on two data-sets, one containing hourly samples from 23395 disks and the other reporting daily samples from 29878‬ disks. Experimental results showed that our approach is well suited to data-sets with different sampling periods, being able to predict hard drive health status up to 45 days before failure.

Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imaging has been largely adopted in sensitive applications, like the diagnosis and follow-up of preterm newborns in neonatal intensive care units. On the other side, state-of-the-art image analysis and pattern recognition approaches have recently proven their ability to fully exploit the rich information contained in these data, making them attractive for the research community. In this work, we present a thorough analysis of recent deep learning networks and training strategies carried out on a vast and challenging multicenter dataset comprising 87 patients with different diseases and gestational ages. These approaches are employed to assess the lung respiratory status from ultrasound images and are evaluated against a reference marker. The conducted analysis sheds some light on this problem by showing the critical points that can mislead the training procedure and proposes some adaptations to the specific data and task. The achieved results sensibly outperform those obtained by a previous work, which is based on textural features, and narrow the gap with the visual score predicted by the human experts.