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Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans. Classification from MRI voxels separately typically does not provide independent evidence towards or against a class; the information relevant for classification is only present in the form of complicated multivariate patterns (or “features”). Deep learning solves this problem by learning a sequence of non-linear transformations that result in feature representations that are better suited to classification. Such learned features have been shown to drastically outperform hand-engineered features in computer vision and audio analysis domains. However, applying the deep learning approach to the task of MRI classification is extremely challenging, because it requires a very large amount of data which is currently not available. We propose to instead use a three dimensional scattering transform, which resembles a deep convolutional neural network but has no learnable parameters. Furthermore, the scattering transform linearizes diffeomorphisms (due to e.g. residual anatomical variability in MRI scans), making the different disease states more easily separable using a linear classifier. In experiments on brain morphometry in Alzheimer's disease, and on white matter microstructural damage in HIV, scattering representations are shown to be highly effective for the task of disease classification. For instance, in semi-supervised learning of progressive versus stable MCI, we reach an accuracy of 82.7%. We also present a visualization method to highlight areas that provide evidence for or against a certain class, both on an individual and group level.

Introduction: Cervical cancer is a significant public health problem worldwide. The development of cervical neoplasms is associated with persistent infection by oncogenic subtypes of the HPV virus, which are responsible for around 70% of cervical cancers.A study carried out in Brazil between 2002 and 2021 recorded 133,429 deaths from cervical cancer.. Methodology: An observational, retrospective, cross-sectional, quantitative study was carried out using data collected by the Cancer Registry Service of the Haroldo Juaçaba Hospital/Ceará Cancer Institute (HHJ/ICC) over 22 years. Results: The sample consisted of 9096 women. The mean age was 51.4 ± 15.5, ranging from 15 to 100, with a median age of 60 years. Median overall survival was 59.77. The most frequent histological type was squamous cell carcinoma, with 8023 (88.2 %) cases. Around 25 % of patients (n = 2270) entered the service with stage 4A. The most common treatment was radiotherapy combined with chemotherapy (n = 3270, 35.9 %), followed by surgical removal (n = 1909, 21.0 %).In multivariate analysis, age (p = 0.019), race (p = 0.016), low level of education (p