Apparatus and method for separating a semiconductor wafer Patent

Separation of semiconductor wafer into chips bounded by scribe line

Improved method of dicing integrated circuit wafers into chips

Method employing a pressure chamber is used for dicing semiconductor single-crystal wafers, containing integrated circuits, into small chips along pre-scribed lines. Uniform bending of the scribed wafer over the convex surface of a perforated hemisphere, breaks it cleanly into individual chips without damaging the circuits

Cancer rate of the indeterminate lesions at low or high risk according to italian system for reporting of thyroid FNA

Background: Italian consensus for the classification and reporting of thyroid cytology (ICCRTC) has been used in almost all Italian institutions since 2014. High reliability of ICCRTC in classifying low and high risk indeterminate nodules (Tir 3A and Tir 3B, respectively) was demonstrated. Here we reviewed our casuistry of thyroid indeterminate lesions to analyze the histologic outcome. Methods: All lesions undergone FNA and final histology at S. Andrea Hospital of Rome after a cytologic assessment of Tir 3A and Tir 3B, according to ICCRTC, were included in the study. Results: A number of 157 indeterminate FNA was found after the introduction of ICCRTC. Of these, 75 undergone surgery and were finally included for the study. At histology we found a 33.3% of cancers and a 67.7% of benign lesions. Out of the overall series, 25 were classified as Tir 3A and 50 as Tir 3B. Cancer rate observed in Tir 3A (1/25, 4%) was significantly (p = 0.0002) lower than that of Tir 3B (24/50, 48%). No significant difference was found in age and size between the two subcategories. Conclusions: We confirm in our series that Italian consensus for the classification and reporting of thyroid cytology allows to discriminate indeterminate lesions at low and high risk of malignancy

Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman's rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems

A double-blind, randomized parallel-group, efficacy and safety study of intramuscular S-adenosyl-L-methionine 1,4-butanedisulphonate (SAMe) versus imipramine in patients with major depressive disorder.

S-adenosyl-L-methionine (SAMe) is a natural substance which constitutes the most important methyl donor in transmethylation reactions in the central nervous system. Several clinical trials have shown that SAMe possesses an antidepressant activity. This multicentre study was carried out to confirm both efficacy and safety of SAMe in the treatment of major depression. SAMe was given intramuscularly (i.m.) at a dose of 400 mg/d, double-blind, vs. 150 mg/d oral Imipramine (IMI) in patients with a diagnosis of major depressive episode, with a baseline score on the 21-item Hamilton Depression Rating Scale (HAMD) of >or=18. A total of 146 patients received SAMe whereas 147 received IMI for a period of 4 wk. The two main efficacy measures were endpoint HAMD score and percentage of responders to Clinical Global Impression (CGI) at week 4. Secondary efficacy measures were the final Montgomery-Asberg Depression Rating Scale (MADRS) scores and the response rate intended as a fall in HAMD scores of at least 50% with respect to baseline. The analysis of safety and tolerability was conducted in all treated patients. SAMe and IMI did not differ significantly on any efficacy measure, either main or secondary. Adverse events were significantly less in patients treated with SAMe compared to those treated with IMI. These data show 400 mg/d i.m. SAMe to be comparable to 150 mg/d oral IMI in terms of antidepressive efficacy, but significantly better tolerated. These findings suggest interesting perspectives for the use of SAMe in depression

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BackgroundThe role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.MethodsLungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.ResultsDespite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.ConclusionsVisual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts

Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters

Riduzione dell'impatto ambientale di un impianto di produzione elettroiniettori tramite rigenerazione dell'Exxol

All'interno della tesi viene preso in considerazione il caso della produzione di elettroiniettori, per il cui controllo di qualità, viene impiegato un idrocarburo speciale denominato commercialmente Exxol. Tale idrocarburo veniva deteriorato durante l'utilizzo e doveva essere smaltito con impatto ambientale di una certa rilevanza. Il lavoro di tesi è stato diretto verso l'installazione e la messa a punto di un impianto per il recupero e la rigenerazione di tale sostanza con conseguente riduzione dei costi di approvvigionamento e di smaltimento

A deep Convolutional Neural Network classifier for breast density assessment: optimization and explainability

This master thesis deals with the optimization and explainability of a deep Residual Convolutional Neural Network classifier developed to assess breast density, defined as the amount of fibroglandular tissue compared to fat tissue visible on a digital mammogram. It is a risk factor for breast cancer and a parameter on which the dosimetric index depends on. An algorithm, based on deep learning methods, has been already developed to automatically classify mammograms into the 4 density classes reported in BIRADS atlas. A major problem of Deep Neural Network is their lack of transparency due to their deep multi-layer nonlinear structure. In the medical field, assessing trust in the model is fundamental for a potential application in clinical practice. The concept of explainability consists in having a look into a black box-like network to make clear the reasons behind predictions and understand its behavior. There is no well-established method and a study on explainability is completely missing in similar works, therefore two possible ways have been explored. Primarily, the classifier has been trained in different conditions to study how the output varies with the input. In this phase, preprocessing step, model architecture and class distribution in the dataset have been taken into account as factors that influence the classifier performance. Then, off-line visualization techniques have been used. Class Activation Maps, i.e. images that highlight the regions of the mammogram on which the attention of the algorithm is focused on, have been generated with a gradient-based method and qualitatively evaluated. These analyses have led to a better understanding of how the algorithm works and to an improvement in its classification performance in terms of accuracy