Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computer-based classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases – particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns

Towards Interactive Breast Tumor Classification Using Transfer Learning

The diagnosis of breast cancer relies on the accurate classification of morphological subtypes in histological sections. Recent advances in image analysis using convolutional neural networks have yielded promising automated methods for this classification task. These networks are usually trained from scratch and depend on hours-long training with thousands of labeled examples to produce good results. Once trained these methods can not easily be adapted in cases of misclassification or to novel tasks. We aim to develop methods that can quickly be adapted in an interactive way. As a first step in this direction we present a classification method that enables fast training with a limited number of samples and achieves state-of-the-art results

Patched completed local binary pattern is an effective method for neuroblastoma histological image classification

© Springer Nature Singapore Pte Ltd. 2018. Neuroblastoma is the most common extra cranial solid tumour in children. The histology of neuroblastoma has high intra-class variation, which misleads existing computer-aided histological image classification methods that use global features. To tackle this problem, we propose a new Patched Completed Local Binary Pattern (PCLBP) method combining Sign Binary Pattern (SBP) and Magnitude Binary Pattern (MBP) within local patches to build feature vectors which are classified by k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The advantage of our method is extracting local features which are more robust to intra-class variation compared to global ones. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our experiments show the proposed method improves the weighted average F-measure by 1.89% and 0.81% with k-NN and SVM classifiers, respectively

MOLECULAR DETECTION OF RANGELIA VITALII IN A DOG FROM THE CURITIBA METROPOLITAN REGION, PARANA STATE, SOUTHERN BRAZIL

Rangeliosis, caused by Rangelia vitalii, is known to causes apathy, intermittent fever, ear’s margin bleeding and anemia. Herein, we report the first molecular detection of R. vitalii infecting a dog from the Curitiba metropolitan region, Paraná State, southern Brazil. A four-year-old male mixed-breed neighborhood dog, presenting apathy, weight loss, and ear’s margin bleeding, was referred for clinical care at the Veterinary Teaching Hospital of the Universidade Federal do Paraná. Sequencing of the piroplasm positive sample showed ≥99% identity with R. vitalii 18S rDNA sequence deposited in GenBank. Further studies are needed to elucidate the pathogenicity, competence vector of ticks and epidemiology of this protozoa the State of Paraná