Multi-modal Latent Diffusion

Multi-modal data-sets are ubiquitous in modern applications, and multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of the different modalities. However, existing approaches suffer from a coherence-quality tradeoff, where models with good generation quality lack generative coherence across modalities, and vice versa. We discuss the limitations underlying the unsatisfactory performance of existing methods, to motivate the need for a different approach. We propose a novel method that uses a set of independently trained, uni-modal, deterministic autoencoders. Individual latent variables are concatenated into a common latent space, which is fed to a masked diffusion model to enable generative modeling. We also introduce a new multi-time training method to learn the conditional score network for multi-modal diffusion. Our methodology substantially outperforms competitors in both generation quality and coherence, as shown through an extensive experimental campaign

MINDE: Mutual Information Neural Diffusion Estimation

In this work we present a new method for the estimation of Mutual Information (MI) between random variables. Our approach is based on an original interpretation of the Girsanov theorem, which allows us to use score-based diffusion models to estimate the Kullback Leibler divergence between two densities as a difference between their score functions. As a by-product, our method also enables the estimation of the entropy of random variables. Armed with such building blocks, we present a general recipe to measure MI, which unfolds in two directions: one uses conditional diffusion process, whereas the other uses joint diffusion processes that allow simultaneous modelling of two random variables. Our results, which derive from a thorough experimental protocol over all the variants of our approach, indicate that our method is more accurate than the main alternatives from the literature, especially for challenging distributions. Furthermore, our methods pass MI self-consistency tests, including data processing and additivity under independence, which instead are a pain-point of existing methods

Apoptosis, a Metabolic "Head-to-Head" between Tumor and T Cells: Implications for Immunotherapy

Induction of apoptosis represents a promising therapeutic approach to drive tumor cells to death. however, this poses challenges due to the intricate nature of cancer biology and the mechanisms employed by cancer cells to survive and escape immune surveillance. furthermore, molecules released from apoptotic cells and phagocytes in the tumor microenvironment (TME) can facilitate cancer progression and immune evasion. apoptosis is also a pivotal mechanism in modulating the strength and duration of anti-tumor T-cell responses. combined strategies including molecular targeting of apoptosis, promoting immunogenic cell death, modulating immunosuppressive cells, and affecting energy pathways can potentially overcome resistance and enhance therapeutic outcomes. thus, an effective approach for targeting apoptosis within the TME should delicately balance the selective induction of apoptosis in tumor cells, while safeguarding survival, metabolic changes, and functionality of T cells targeting crucial molecular pathways involved in T-cell apoptosis regulation. enhancing the persistence and effectiveness of T cells may bolster a more resilient and enduring anti-tumor immune response, ultimately advancing therapeutic outcomes in cancer treatment. this review delves into the pivotal topics of this multifaceted issue and suggests drugs and druggable targets for possible combined therapies

One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models

Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. However, they typically achieve lower sample quality compared to state-of-the-art score-based diffusion models (DMs). This paper provides a significant step in the direction of addressing this limitation. The idea is to borrow one of the strengths of score-based DMs, which is the ability to perform accurate density estimation in low-density regions and to address manifold overfitting by means of data mollification. We connect data mollification through the addition of Gaussian noise to Gaussian homotopy, which is a well-known technique to improve optimization. Data mollification can be implemented by adding one line of code in the optimization loop, and we demonstrate that this provides a boost in generation quality of likelihood-based GMs, without computational overheads. We report results on image data sets with popular likelihood-based GMs, including variants of variational autoencoders and normalizing flows, showing large improvements in FID score

Continuous-Time Functional Diffusion Processes

We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework to describe the forward and backward dynamics, and several extensions to derive practical training objectives. These include infinite-dimensional versions of Girsanov theorem, in order to be able to compute an ELBO, and of the sampling theorem, in order to guarantee that functional evaluations in a countable set of points are equivalent to infinite-dimensional functions. We use FDPs to build a new breed of generative models in function spaces, which do not require specialized network architectures, and that can work with any kind of continuous data. Our results on real data show that FDPs achieve high-quality image generation, using a simple MLP architecture with orders of magnitude fewer parameters than existing diffusion models.Comment: Under revie

Treatment of osteolytic solitary painful osseous metastases with radiofrequency ablation or cryoablation: a retrospective study by propensity analysis

The present study aimed to measure the improvement in pain relief and quality of life in patients with osteolytic solitary painful bone metastasis treated by cryoablation (CA) or radiofrequency ablation (RFA). Fifty patients with solitary osteolytic painful bone metastases were retrospectively studied and selected by propensity analysis. Twenty-five patients underwent CA and the remaining twenty-five underwent RFA. Pain relief, in terms of complete response (CR), the number of patients requiring analgesia and the changes in self-rated quality of life (QoL) were measured following the two treatments. Thirty-two percent of patients treated by CA experienced a CR at 12 weeks versus 20% of patients treated by RFA. The rate of CR increased significantly with respect to baseline only in the group treated by CA. In both groups there was a significant change in the partial response with respect to baseline (36% in the CA group vs. 44% in the RFA group). The recurrence rate in the CA and RFA groups was 12% and 8%, respectively. The reduction in narcotic medication requirements with respect to baseline was only significant in the group treated by CA. A significant improvement in self-rated QoL was observed in both groups. The present study seems to suggest that CA only significantly improves the rate of CR and decreases the requirement of narcotic medications. Both CA and RFA led to an improvement in the self-rated QoL of patients after the treatments. However, the results of the present study should be considered as preliminary and to serve as a framework around which future trials may be designed

Treatment of Solitary Painful Osseous Metastases with Radiotherapy, Cryoablation or Combined Therapy: Propensity Matching Analysis in 175 Patients

aim of this study was to identify outcomes in pain relief and quality of life in patients with a solitary painful osseous metastasis treated by radiotherapy, cryoablation or the combination using a propensity score matching study design

Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net

Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of organs at risk (OARs) and target areas is essential for effective treatment planning. Intensity Modulated Radiotherapy (IMRT) techniques, such as Total Marrow Irradiation (TMI) and Total Marrow and Lymph node Irradiation (TMLI), provide more precise radiation delivery compared to Total Body Irradiation (TBI). However, these techniques require time-consuming manual segmentation of structures in Computerized Tomography (CT) scans by the Radiation Oncologist (RO). In this paper, we present a deep learning-based auto-contouring method for segmenting Planning Target Volume (PTV) for TMLI treatment using the U-Net architecture. We trained and compared two segmentation models with two different loss functions on a dataset of 100 patients treated with TMLI at the Humanitas Research Hospital between 2011 and 2021. Despite challenges in lymph node areas, the best model achieved an average Dice score of 0.816 for PTV segmentation. Our findings are a preliminary but significant step towards developing a segmentation model that has the potential to save radiation oncologists a considerable amount of time. This could allow for the treatment of more patients, resulting in improved clinical practice efficiency and more reproducible contours