Breast Cancer Tissue Classification from Multiple Annotators using Chained Deep Learning Approaches.

Breast cancer is one of the principal causes of cancer death worldwide. The biopsy diagnosis is non-trivial, and specialists often disagree on the final diagnosis. Thus, Computer-aided Diagnosis-(CAD) systems favor the efficiency of this process while reducing the diagnosis time.

Unsupervised Anomaly Detection by Learning Elastic Transformations Within an Autoencoder Approach.

Machine learning has approached magnetic resonance image (MRI) analysis using multiple techniques, including deep supervised learning methodologies, where lesions such as tumors or features associated with defined pathologies have been identified satisfactorily. However, many of these models require a certain amount of labeled data which access is usually restricted, due to the protection of health information. Furthermore, those methodologies are focused only on the pathologies considered in the training process, causing the model to ignore other kinds of brain lesions.

Efficient Lung Segmentation from Chest Radiographs using Transfer Learning and Lightweight Deep Architecture.

Lung delineation constitutes a critical preprocessing stage for X-ray-based diagnosis and follow-up. However, automatic lung segmentation from chest radiographs (CXR) poses a challenging problem due to anatomical structures' varying shapes and sizes, the differences between radio-opacity, contrast, and image quality, and the requirement of complex models for automatic detection of regions of interest. This work proposes the automated lung segmentation methodology DenseCX, based on U-Net architectures and transfer learning techniques.

Biogeographic Ancestry Analysis of Microtia Patients in Colombia using Nonlinear Probabilistic Clustering.

Population composition is crucial in exploring genetic associations and investigating conditions and diseases. Single nucleotide polymorphisms (SNPs) are the subject of extensive studies, as they represent the most prevalent genetic variability in the human population. This work proposes a hierarchical framework for nonlinear probabilistic clustering of individuals with mixed ancestry population components.

3D probe localization from 2D ultrasound images using an RFF-enhanced deep neural network.

Peripheral nerve blocking (PNB) via ultrasound (US) imaging offers the advantages of non-invasiveness, nonionizing radiation, and real-time visualization. However, the high cost of 3D US makes the clinicians to imagine the anatomical volume from 2D sections. Consequently, there is a need to develop a tool capable of predicting the trajectory of a US probe and reconstructing a 3D volume.