Atti del VI Convegno Nazionale Società Italiana di Scienze Sensoriali

Il volume raccoglie più di cinquanta contributi presentati in occasione del VI Convegno Nazionale della Società Italiana di Scienze Sensoriali (Bologna, 30 novembre-2 dicembre 2016) su temi che vanno dalle differenze individuali nelle preferenze alimentari e responsi affettivi ai prodotti alimentari fino alla descrizione dei prodotti alimentari e alle relazioni tra studi sensoriali e strumentali. Sono inoltre raccolti qui i contributi risultati vincitori del Premio SISS per Giovani Ricercatori 2016 e del Premio Adacta International in Sensory & Consumer Science in memoria di Annalisa Intermoia. Ad arricchire il volume, inoltre, il contributo presentato nell’ambito del progetto di Student Exchange SISS/AEPAS della European Sensory Science Society

Deep learning algorithms for tumor detection in screening mammography

Population-wide mammography screening was fully implemented in Sweden in 1997. The implementation has helped to identify breast cancer at earlier stages and thereby lowered mortality by 30-40%. However, it still has its limitations, many studies have shown a discrepancy between radiologist when assessing mammographic examinations. Additionally, women with very dense breasts have a lower mammographic sensitivity and cancers are easily missed. There is also a shortage on breast radiologists and the workload is increasing due to more women being screened. These challenges could be addressed with the help of artificial intelligence systems. The artificial intelligence system can serve both as an assistant to replace one radiologist in a double-reading setting and as a tool to triage women with a high risk of breast cancer for additional screening using other modalities. In this thesis we used data from two cohorts: the cohort of screen aged women (CSAW) and the ScreenTrust MRI cohort. The primary objectives were to establish performance benchmarks based on radiologists recorded assessments (study I), compare the diagnostic performance of various AI CAD systems (study II), investigate differences and similarities in false assessments between AI CAD and radiologists (study III), and evaluate the potential of artificial intelligence in triaging women for complementary MRI screening (study IV). The data for studies I-III were obtained from CSAW, while the data for study IV were obtained from the MRI ScreenTrust cohort. CSAW is a collection of data from Stockholm County between the years of 2008 and 2015. Study I was a retrospective multicenter cohort study that examined radiologist performance benchmarks in screening mammography. Operating performance was assessed in terms of abnormal interpretation rate, false negative rate, sensitivity, and specificity. Measures were determined for each quartile of radiologists classified according to performance, and performance was evaluated overall and by different tumor characteristics. The study included a total of 418,041 women and 1,186,045 digital mammograms, and involved 110 radiologists, of which 24 were defined as high-volume readers. Our analysis revealed significant differences in performance between highvolume readers, as well as a variability in sensitivity based on tumor characteristics. This study was presented during the 2019 annual meeting of the Radiological Society of North America, and was awarded the Trainee research prize that same year. Study II was a retrospective case-control study that evaluated the performance of three commercial algorithms. We performed an external evaluation of these algorithms and compared the retrospective mammography assessments of radiologists with those of the algorithms. Operating performance was determined in terms of abnormal interpretation rate, false negative rate, sensitivity, specificity and the AUC. The study included 8,805 women, of whom 740 women had cancer, and a random sample of 8,066 healthy controls. There were 25 radiologists involved. For a binary decision, the cutpoint was defined by the mean specificity of the original first-reader radiologists (96.6%). Our findings showed that one AI algorithm outperformed the other AI algorithm and the original first-reader radiologists. This study was presented during the 2020 annual meeting of the European Society of Radiology. Study III was a retrospective case-control study that evaluated the differences and similarities in false assessments between an artificial intelligence system and a human reader in screening mammography. In this study we included 714 screening examinations for women diagnosed with breast cancer and 8,003 randomly selected healthy controls. The abnormality threshold was predefined from study II. We examined how false positive and false negative assessments by AI CAD and the first radiologist, were associated with breast density, age and tumor characteristics. Our findings showed that AI makes fewer false negative assessments than radiologists. Combining AI with a radiologist resulted in the most pronounced decrease in false negative assessments for high-density women and women over the age of 55. This study was presented at the 2021 annual meeting of the Radiological Society of North America. Study IV is a randomized clinical trial that aims to investigate the effect of applying deep learning methods to select women for MRI-based breast cancer screening. The study examines how effectively AI can identify women who should be offered a complementary MRI screening based on their likelihood of having cancer that is not visible on regular mammography. The results reported in this thesis are preliminary and based on examinations from April 1, 2021 to December 31, 2022. During the indicated time period, 481 MRI examinations have been completed, and 28 cancers have been detected, yielding a cancer detection rate of 58.2 per 1,000 examinations. Although, the trial is still ongoing, the inter-rim results suggest that using AI-based selection for supplemental MRI screening can lead to a higher rate of cancer detection than that reported for density-based selection methods. In conclusion, we have shown that the use of AI for breast cancer detection can increase precision and efficiency in mammography screening

Phase separation effects and the nematic-isotropic transition in polymer and low molecular weight liquid crystals doped with nanoparticles

Properties of the nematic–isotropic phase transition in polymer and low molecular weight liquid crystals doped with nanoparticles have been studied both experimentally and theoretically in terms of molecular mean-field theory. The variation of the transition temperature and the transition heat with the increasing volume fraction of CdSe quantum dot nanoparticles in copolymer and low molecular weight nematics has been investigated experimentally and the data are interpreted using the results of the molecular theory which accounts for a possibility of phase separation when the system undergoes the nematic–isotropic transition. The theory predicts that the nematic and isotropic phases with different concentrations of nanoparticles may coexist over a broad temperature range, but only if the nanoparticle volume fraction exceeds a certain threshold value which depends on the material parameters. Such unusual phase separation effects are determined by the strong interaction between nanoparticles and mesogenic groups and between nanoparticles themselves

Society-in-the-Loop: Programming the Algorithmic Social Contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

Model Checking One-clock Priced Timed Automata

We consider the model of priced (a.k.a. weighted) timed automata, an extension of timed automata with cost information on both locations and transitions, and we study various model-checking problems for that model based on extensions of classical temporal logics with cost constraints on modalities. We prove that, under the assumption that the model has only one clock, model-checking this class of models against the logic WCTL, CTL with cost-constrained modalities, is PSPACE-complete (while it has been shown undecidable as soon as the model has three clocks). We also prove that model-checking WMTL, LTL with cost-constrained modalities, is decidable only if there is a single clock in the model and a single stopwatch cost variable (i.e., whose slopes lie in {0,1}).Comment: 28 page

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i