Gravity Network for end-to-end small lesion detection

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks

Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification

Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor images recognized from the electroencephalogram (EEG) to control commands. EEG patterns of different imagination tasks, e.g. hand and foot movements, are effectively classified with machine learning techniques using band power features. Recently, also Convolutional Neural Networks (CNNs) that learn both effective features and classifiers simultaneously from raw EEG data have been applied. However, CNNs have two major drawbacks: (i) they have a very large number of parameters, which thus requires a very large number of training examples; and (ii) they are not designed to explicitly learn features in the frequency domain. To overcome these limitations, in this work we introduce Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass and depthwise convolutional filters. Experimental results obtained on the publicly available BCI Competition IV Dataset 2a show that our approach outperforms reference methods in terms of classification accuracy

Zinc-finger-based transcriptional repression of rhodopsin in a model of dominant retinitis pigmentosa

Despite the recent success of gene-based complementation approaches for genetic recessive traits, the development of therapeutic strategies for gain-of-function mutations poses great challenges. General therapeutic principles to correct these genetic defects mostly rely on post-transcriptional gene regulation (RNA silencing). Engineered zinc-finger (ZF) protein-based repression of transcription may represent a novel approach for treating gain-of-function mutations, although proof-of-concept of this use is still lacking. Here, we generated a series of transcriptional repressors to silence human rhodopsin (hRHO), the gene most abundantly expressed in retinal photoreceptors. The strategy was designed to suppress both the mutated and the wild-type hRHO allele in a mutational-independent fashion, to overcome mutational heterogeneity of autosomal dominant retinitis pigmentosa due to hRHO mutations. Here we demonstrate that ZF proteins promote a robust transcriptional repression of hRHO in a transgenic mouse model of autosomal dominant retinitis pigmentosa. Furthermore, we show that specifically decreasing the mutated human RHO transcript in conjunction with unaltered expression of the endogenous murine Rho gene results in amelioration of disease progression, as demonstrated by significant improvements in retinal morphology and function. This zinc-finger-based mutation-independent approach paves the way towards a ‘repression–replacement’ strategy, which is expected to facilitate widespread applications in the development of novel therapeutics for a variety of disorders that are due to gain-of-function mutations

Bit Error Recovery in ECOC Systems through LDPC Codes

Error Correcting Output Coding (ECOC) is a widely used and successful technique that implements a classification system by splitting a multiclass problem into a set of dichotomies according to a coding matrix. In this paper we propose a new approach for the ECOC systems based on a well-known family of error correcting codes in the Coding Theory: the Low Density Parity Check (LDPC) codes. The goal is twofold: first, to introduce a new coding strategy for determining the code words to be collected in a coding matrix. Second, to exploit the algebraic properties of LDPC codes for recovering the bit errors in the output word and increasing the performance of the classification system. We compare the proposed technique with other commonly used coding strategies on some benchmark data sets achieving very interesting results

A Boosting-Based Approach to Refine the Segmentation of Masses in Mammography

In this paper we present an algorithm for finding an accurate estimate of the contour of masses in mammograms. We assume that a rough estimate of the region containing the mass is known: in particular it is available the location of an area inside the mass (core) and a closed curve beyond which the mass does not extend. The proposed method employs a boosting-based classifier trained on the core and on a background region beyond the external contour, so that it provides an accurate estimate of the mass contour by classifying unlabeled pixels between the core and the external contour. The proposed approach is useful not only for automatic localization of mass contour, but also as a powerful tool during annotation of mammograms, given that an user provides interactively an estimate for the core and the external contour of the mass. The approach has been verified on a set of mammograms showing very encouraging results

A Boosting-Based Approach to Refine the Segmentation of Masses in Mammography

In this paper we present an algorithm for finding an accurate estimate of the contour of masses in mammograms. We assume that a rough estimate of the region containing the mass is known: in particular it is available the location of an area inside the mass (core) and a closed curve beyond which the mass does not extend. The proposed method employs a boosting-based classifier trained on the core and on a background region beyond the external contour, so that it provides an accurate estimate of the mass contour by classifying unlabeled pixels between the core and the external contour. The proposed approach is useful not only for automatic localization of mass contour, but also as a powerful tool during annotation of mammograms, given that an user provides interactively an estimate for the core and the external contour of the mass. The approach has been verified on a set of mammograms showing very encouraging results

An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules

Two class classifiers are used in many complex problems in which the classification results could have serious consequences. In such situations the cost for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimates of the a posteriori probabilities are not reliable