• Empirical evidence that frozen Stable Diffusion features contain signals useful for distinguishing real and AI-generated objects. • A comprehensive analysis of feature representations extracted from multiple lay- ers of the diffusion U-Net. • Object-level study on COCO and Pascal VOC against DALL-E 3, Midjourney, and Stable Diffusion v1.4/v1.5. • Decoder features extracted at spatial resolutions of 16 ×16 and 32 ×32 show the strongest discriminative behavior under the evaluated conditions.

Circle detection plays a pivotal role in computer vision, underpinning applications from industrial inspection and bioinformatics to autonomous driving. Traditional methods, however, often struggle with real–world complexities, as they demand extensive parameter tuning and adaptation across different domains. In this paper, we present the Synthetic Circle Dataset (SynCircle), a large synthetic image dataset designed to train a YOLO v10 network for circle detection. The YOLO v10 network, pre–trained solely on synthetic data, demonstrates remarkable off–the–shelf performance that surpasses conventional methods in various practical scenarios. Furthermore, we show that incorporating just a few labeled real images for fine–tuning can significantly boost performance, reducing the need for large annotated datasets. To promote reproducibility and streamline adoption, we publicly release both the trained YOLO v10 weights and the full SynCircle dataset.

We propose a novel approach for skin lesion classification that leverages a transformer architecture to integrate diverse clinical information (dermoscopic images, segmentation maps, and patient clinical information) for more accurate diagnosis.By incorporating binary semantic segmentation maps as input, we directly provide the model with border details critical for distinguishing between benign and malignant lesions.This integration improves classification performance compared to models that use only dermoscopic images or clinical data.To the best of our knowledge, this is the first application of segmentation maps to enhance skin lesion classification.Our experiments on the ISIC dataset yield promising results, highlighting the potential of combining advanced transformer models with multimodal data for improved dermatological diagnostics.

Recently, generative models for images have garnered remarkable attention, due to their effective generalization ability and their capability to generate highly detailed and realistic content. Indeed, the success of generative networks (e.g., BigGAN, StyleGAN, Diffusion Models) has driven researchers to develop increasingly powerful models. As a result, we have observed an unprecedented improvement in terms of both image resolution and realism, making generated images indistinguishable from real ones. In this work, we focus on a family of generative models known as Stable Diffusion Models (SDMs), which have recently emerged due to their ability to generate images in a multimodal setup (i.e., from a textual prompt) and have outperformed adversarial networks by learning to reverse a diffusion process. Given the complexity of these models that makes it hard to retrain them, researchers started to exploit pre-trained SDMs to perform downstream tasks (e.g., classification and segmentation), where semantics plays a fundamental role. In this context, understanding how well the model preserves semantic information may be crucial to improve its performance. This paper presents an approach aimed at providing insights into the properties of a pre-trained SDM through the semantic lens. In particular, we analyze the features extracted by the U-Net within a SDM to explore whether and how the semantic information of an image is preserved in its internal representation. For this purpose, different distance measures are compared, and an ablation study is performed to select the layer (or combination of layers) of the U-Net that best preserves the semantic information. We also seek to understand whether semantics are preserved when the image undergoes simple transformations (e.g., rotation, flip, scale, padding, crop, and shift) and for a different number of diffusion denoising steps. To evaluate these properties, we consider popular benchmarks for semantic segmentation tasks (e.g., COCO, and Pascal-VOC). Our experiments suggest that the first encoder layer at resolution effectively preserves semantic information. However, increasing inference steps (even for a minimal amount of noise) and applying various image transformations can affect the diffusion U-Net’s internal feature representation. Additionally, we propose some examples taken from a video benchmark (DAVIS dataset), where we investigate if an object instance within a video preserves its internal representation even after several frames. Our findings suggest that the internal object representation remains consistent across multiple frames in a video, as long as the configuration changes are not excessive.

The ambition to create increasingly realistic images has driven researchers to develop increasingly powerful models, capable of generalizing and generating high-resolution images, even in a multimodal setup (e.g., from textual input). Among the most recent generative networks, Stable Diffusion Models (SDMs) have achieved state-of-the-art showing great generative capabilities but also a high degree of complexity, both in terms of training and interpretability. Indeed, the impressive generalization capability of pre-trained SDMs has pushed researchers to exploit their internal representation to perform downstream tasks (e.g., classification and segmentation). Understanding how well the model preserves semantic information is fundamental to improve its performance. Our approach, namely Diff-Props, analyses the features extracted from the U-Net within Stable Diffusion Model to unveil how Stable Diffusion retains semantic information of an image in a pre-trained setup. Exploiting a set of different distance metrics, Diff-Props aims to analyse how features at different depths contribute to preserving the meaning of the objects in the image.

In an era characterized by rapid technological advancement and rising customer expectations, accurate ticket classification in banking customer service emerges as a critical necessity. In this context, we designed a comprehensive ticket classification pipeline, leveraging a real-world dataset comprising 4,243 chat-based user requests, classified into ten distinct classes, provided by MPS Bank. Our approach proposes a complete data processing pipeline with an exploration of two text classification methodologies: BERT (Bidirectional Encoder Representations from Transformers) and TF-IDF (Term Frequency - Inverse Document Frequency) with SVM (Support Vector Machine). The experiments highlight that both models have considerable potential, promising substantial improvements in the operational efficiency of customer support, ultimately increasing the overall quality of service.

The importance of kidney biopsy, a medical procedure in which a small tissue sample is extracted from the kidney for examination, is increasing due to the rising incidence of kidney disorders. This procedure helps diagnosing several kidney diseases which are cause of kidney function changes, as well as guiding treatment decisions, and evaluating the suitability of potential donor kidneys for transplantation. In this work, a deep learning system for the automatic segmentation of glomeruli in biopsy kidney images is presented. A novel cross–species transfer learning approach, in which a semantic segmentation network is trained on mouse kidney tissue images and then fine–tuned on human data, is proposed to boost the segmentation performance. The experiments conducted using two deep semantic segmentation networks, MobileNet and SegNeXt, demonstrated the effectiveness of the cross–species pre–training approach leading to an increased generalization ability of both models.

Early detection of skin cancers greatly increases patients' chances of recovery.To support dermatologists in this diagnosis, many decision support systems based on Convolutional Neural Networks have recently been proposed to segment the lesion and classify it.The use of the information coming from the segmentation, as an additional input to the classifier, proved to be fundamental to increase its performance and, in fact, the shape of the lesion is of diagnostic importance unanimously recognized by clinicians.However, in the ISIC database, the public reference dataset that collects a huge number of skin lesion images, all samples are labeled for classification but only a very small fraction of them are also labeled for segmentation.To overcome this limitation, the present paper proposes a weakly supervised approach to extract the segmentation label maps of approximately 43,000 ISIC images, used to train a segmentation network, with very promising performance.

Deep learning (DL) is widely applied in biomedical image processing nowadays.In this paper, we propose the use of DL architectures for glomerulus segmentation in histopathological images of mouse kidneys.Indeed, in humans, the analysis of the glomeruli is fundamental to decide on the transplantability of the organ.However, no datasets with human samples are publicly available.Therefore, obtaining good segmentation performance on the kidneys of mice could be the first step for a transfer learning approach to humans.We compared the use of two well-known architectures for image segmentation, namely MobileNet and DeepLab V2.Both models showed very promising results.

Anisoptera are a suborder of insects belonging to the order of Odonata, commonly identified with the generic term dragonflies. They are characterized by a long and thin abdomen, two large eyes, and two pairs of transparent wings. Their ability to move the four wings independently allows dragonflies to fly forwards, backwards, to stop suddenly and to hover in mid–air, as well as to achieve high flight performance, with speed up to 50 km per hour. Thanks to these particular skills, many studies have been conducted on dragonflies, also using machine learning techniques. Some analyze the muscular movements of the flight to simulate dragonflies as accurately as possible, while others try to reproduce the neuronal mechanisms of hunting dragonflies. The lack of a consistent database and the difficulties in creating valid tools for such complex tasks have significantly limited the progress in the study of dragonflies. We provide two valuable results in this context: first, a dataset of carefully selected, pre–processed and labeled images, extracted from videos, has been released; then some deep neural network models, namely CNNs and LSTMs, have been trained to accurately distinguish the different phases of dragonfly flight, with very promising results.

During the last decade, deep learning and Convolutional Neural Networks (CNNs) have produced a devastating impact on computer vision, yielding exceptional results on a variety of problems, including analysis of medical images. Recently, these techniques have been extended to 3D images with the downside of a large increase in the computational load. In particular, state-of-the-art CNNs have been used for brain Nuclear Magnetic Resonance (NMR) imaging, with the aim of estimating the patients’ age. In fact, a large discrepancy between the real and the estimated age is a clear alarm for the onset of neurodegenerative diseases, such as some types of early dementia and Alzheimer’s disease. In this paper, we propose an effective alternative to 3D convolutions that guarantees a significant reduction of the computational requirements for this kind of analysis. The proposed architectures achieve comparable results with the competitor 3D methods, requiring only a fraction of the training time and GPU memory.

Urinary Tract Infections (UTIs) are very common in women, babies and the elderly. The most frequent cause is a bacterium, called Escherichia coli, which usually lives in the digestive system and in the bowel. Infections can target the urethra, bladder or kidneys. Traditional analysis methods, based on human experts' evaluation, are typically used to diagnose UTIs, an error prone and lengthy process, whereas an early treatment of common pathologies is fundamental to prevent the infection spreading to kidneys. This paper presents an image based Automated Bacterial Load Estimator (ABLE) system for the urinoculture screening, that provides quick and traceable results for UTIs. Infections are accurately detected and the bacterial load is evaluated through image processing techniques. First, digital color images of the Petri dishes are automatically captured, and cleaned from noisily elements due to laboratory procedures, then specific spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate evaluation of the infection severity is performed. A dataset of 499 urine samples has been used during the experiments and the obtained results are fully discussed. The ABLE system speeds up the analysis, grants repeatable results, contributes to the process standardization, and guarantees a significant cost reduction.