Recently, object detection in bioimage analysis has achieved interesting progress through deep learning models like those in the vision transformers, enabling high-accuracy predictions for tasks such as lesion detection in radiology, ophthalmology and organ segmentation. However, applying these models in healthcare raises critical challenges related to privacy and explainability. Centralized training is often infeasible due to strict data-sharing constraints, such as patient privacy regulations, while the black-box nature of modern detectors hinders clinician trust and adoption, particularly in clinical decision-making. To address these issues, we introduce BIOFED, a framework for privacy-preserving and explainable object detection specifically tailored for biomedical imaging. The idea behind BIOFED is to integrate distributed training of object detectors (for instance, the models belonging to the YOLO family) across decentralized clinical or research clients using aggregation strategies like FedAvg and FedProx, ensuring sensitive patient data never leaves the client side. Furthermore, BIOFED incorporates an explainability module based on Class Activation Mapping techniques, by generating heatmaps that highlight bioimage regions, like suspicious lesions in a chest X-ray or retinal fundus image that are most influential for model predictions. This combination of federated object detection and explainability enhances transparency, accountability, and trustworthiness in safety-critical bioimaging scenarios.

The lack of broad, high-quality, labeled datasets and, crucially, stringent data protection laws frequently make it difficult to integrate Deep Learning (DL), with medical imaging. These limitations make it impossible to centralize data, which results in models that don't generalize to other institutions (non-IID data). To address this, we introduce FLIP_VIT, a novel framework leveraging Federated Learning (FL) for explainable and privacy-preserving biomedical imaging. FL is a decentralized system where the training algorithm moves to the data. Hospitals (clients) train a model locally on their private data, and only the model updates (weights or gradients) are securely shared and aggregated by a central server using techniques like Federated Avareging (FedAvg) and Federeted Proximal (FedProx) to create a global model. Patient data never leaves its source, ensuring privacy and compliance.

With the increasingly growing use of Natural Language Processing (NLP) techniques, medical text classification is becoming a prominent field of application. The ability to employ model-based classification to medical abstracts, i.e. brief texts describing the condition of a patient, can potentially offer significant aid to medical experts in the diagnosis process. Transformer-based models have demonstrated significant advancements in NLP tasks. In this paper, we aim to evaluate the performance of four transformer models i.e., BERT, DeBERTa, DistilBERT, and RoBERTa, in classifying medical abstract as one of five possible conditions. By utilizing a labeled dataset consisting of five categories, we conduct a fine-tuning process of each model and finally analyze their performance using several metrics, specifically accuracy, precision, recall, F1-score, and graphical results, particularly confusion matrices and loss graphs. Our findings show comparable performance across models in terms of accuracy, F1-Score, loss, and confusion matrices. Furthermore, we make use of explainability techniques, specifically integrated gradients, to interpret model predictions by analyzing the least and most impactful tokens. The results show criticalities such as dataset imbalance, as well as a common tendency of the models to misclassify.

Breast cancer is a kind of cancer that forms in the cells of the breasts. After skin cancer, breast cancer represents the most common cancer diagnosed in women in the United States. As a matter of fact, in January 2022, there are more than 3.8 million women with a history of breast cancer in the United States, this is the reason why there is a need for novel methods for automatic breast cancer screening, with the aim of starting any therapy as quickly as possible to try to limit the proliferation of the disease. In this article, we propose a method aimed at detecting breast cancer through a deep learning network developed by authors. Moreover, the proposed method is able to provide prediction explainability by means of class activation mapping, aimed to automatically highlight the suspicious area on the image. We take into account a way to understand whether the cancer prediction and localization can be considered robust by analyzing the output of two different class activation mapping algorithms. We evaluate the effectiveness of the proposed method by using a dataset composed of 9,016 images obtaining an accuracy equal to 93.5%, thus showing the effectiveness of the proposed network for breast cancer detection and localization.

Skin lesions are any abnormal growths or appearances on the skin, ranging from benign (i.e., non-cancerous) to malignant (i.e., cancerous). The identification of a skin lesion is a crucial task that is carried out in short periods of time to initiate an eventual therapeutic treatment. In this paper, we propose a method for automatic skin lesion detection, implementing Convolutional Neural Networks. Moreover, with the aim of providing a rationale behind the model prediction, we also consider explainability by adopting two different Class Activation Mapping algorithms, which highlight regions in skin images that most contribute to the network’s classification decision. We also include the indices of similarity for further quantitative analysis. Several Convolutional Neural Networks are considered, by obtaining the best results with the MobileNet model, achieving an accuracy equal to 0.935 in skin lesion detection. Moreover, in the experimental analysis, we discuss the effectiveness of Class Activation Mapping algorithms exploited for skin lesion localization.

Background and Objective: Alzheimer’s disease is nowadays the most common cause of dementia. It is a degenerative neurological pathology affecting the brain, progressively leading the patient to a state of total dependence, thus creating a very complex and difficult situation for the family that has to assist him/her. Early diagnosis is a primary objective and constitutes the hope of being able to intervene in the development phase of the disease. Methods: In this paper, a method to automatically detect the presence of Alzheimer’s disease, by exploiting deep learning, is proposed. Five different convolutional neural networks are considered: ALEX_NET, VGG16, FAB_CONVNET, STANDARD_CNN and FCNN. The first two networks are state-of-the-art models, while the last three are designed by authors. We classify brain images into one of the following classes: non-demented, very mild demented and mild demented. Moreover, we highlight on the image the areas symptomatic of Alzheimer presence, thus providing a visual explanation behind the model diagnosis. Results: The experimental analysis, conducted on more than 6000 magnetic resonance images, demonstrated the effectiveness of the proposed neural networks in the comparison with the state-of-the-art models in Alzheimer’s disease diagnosis and localization. The best results in terms of metrics are the best with STANDARD_CNN and FCNN with accuracy, precision and recall between 98% and 95%. Excellent results also from a qualitative point of view are obtained with the Grad-CAM for localization and visual explainability. Conclusions: The analysis of the heatmaps produced by the Grad-CAM algorithm shows that in almost all cases the heatmaps highlight regions such as ventricles and cerebral cortex. Future work will focus on the realization of a network capable of analyzing the three anatomical views simultaneously.

The COVID-19 pandemic has radically changed our daily habits. One of these habits introduced is the use of a face mask, to avoid the spread of the infection, to be used especially in closed and crowded environments. To protect people and to counter the spread of COVID-19, in this paper we propose a method to automatically verify from images whether people are wearing a face mask. We designed a deep learning network to classify whether an image under analysis is with a mask or without a face mask. With the aim to provide explainability to the classifier decision, the proposed method is able to localise the areas of the image under analysis symptomatic of a certain prediction: in this way it is possible to understand the reason why the model predicts a certain label. The experimental analysis considers 7553 (with mask and without mask) images showing that the proposed approach is able to obtain interesting performances in face mask detection.

Cardiovascular diseases include a very long series of diseases that afflict many people in the world. Many of them can be diagnosed by listening to the heartbeat, however in the face of the large number of patients performing checks, great delays can occur given the few doctors available. In this paper we propose a convolutional neural network aimed to discriminate regular heartbeats from abnormal ones, making a first screening of patients. Moreover we provide classification explainability through activation maps. The experimental analysis consists of 3240 (regular and abnormal) heartbeat phonocardiogram signals, showing the effectiveness of the proposed method.

Nowadays pathologists have to analyse blood cells manually with the aim to diagnose diseases. In order to perform this manual task blood samples must be collected from the patient and then placed on a microscope slide. This slide is studied with the aim to detect the abnormality presence. The manual method used to identify abnormality of blood cells is tedious, prone to human errors and time consuming. Hence there is a need for computer aided system which can analyze blood cells automatically at faster rate with accuracy. Such systems can be designed using image processing techniques. To automatise this process by helping pathologists, in this paper we propose the adoption of deep learning to automatically count and localise red blood cells, white blood cells and platelets by analysing blood microscopic images. We resort to the YOLO object detection model, able to look at the whole image so its predictions are informed by global context in the image. To show the effectiveness of the proposed method we evaluate our model on a dataset composed by 874 microscopic blood images, obtaining interesting results. Furthermore we show several examples related to how the proposed method can be helpful for the pathologists in their real-world work.

The new coronavirus disease (COVID-19), declared a pandemic on 11 March 2020 by the World Health Organization, has caused over 6 million victims worldwide. Because of the rapid spread of the virus, with the aim to perform screening we exploit deep learning model to quickly diagnose altered respiratory conditions. In this paper, we propose a method to recognize and classify cough audio files into three classes to distinguish patients with COVID-19 disease, symptomatic ones and healthy subjects, with the use of a convolutional neural network (CNN). Cough audios were recorded by using a smartphone and its built-in microphone. From cough recordings, we generate spectrogram images and we obtain an accuracy equal to 0.82 with a deep learning network developed by authors. Our method also provides heatmaps, which show the relevant input areas used by the model for the final forecast, and this aspect ensures the explainability of the method.

Nowadays pathologists have to analyse blood cells manually with the aim to diagnose diseases. In order to perform this manual task blood samples must be collected from the patient and then placed on a microscope slide. This slide is studied with the aim to detect the abnormality presence. To automatise this process by helping pathologists, in this paper we propose the adoption of deep learning to automatically count and localise red blood cells, white blood cells and platelets by analysing blood microscopic images. We resort to the YOLO object detection model, able to look at the whole image so its predictions are informed by global context in the image. To show the effectiveness of the proposed method we evaluate our model on a dataset composed by 874 microscopic blood images, obtaining interesting results. Furthermore we show several examples related to how the proposed method can be helpful for the pathologists in their real-world work.

The aim of this study is to evaluate the application of ensemble averaging to the analysis of electromyography recordings under whole body vibratory stimulation. Recordings from Rectus Femoris, collected during vibratory stimulation at different frequencies, are used. Each signal is subdivided in intervals, which time duration is related to the vibration frequency. Finally the average of the segmented intervals is performed. By using this method for the majority of the recordings the periodic components emerge. The autocorrelation of few seconds of signals confirms the presence of a pseudosinusoidal components strictly related to the soft tissues oscillations caused by the mechanical waves.

Through a lumped parameter modelling approach, a dynamical model, which can reproduce the motion of the muscles of a human body standing in different postures during Whole Body Vibrations (WBVs) treatment, has been developed. The key parameters, associated to the dynamics of the motion of the muscles of the lower limbs, have been identified starting from accelerometer measurements. The developed model can be usefully applied to the optimization of WBVs treatments which can effectively enhance muscle activation.