Transitioning between Convolutional and Fully Connected Layers in Neural Networks

Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified "transition" module which learns global average pooling layers from filters of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.Comment: This work is to appear at the 3rd workshop on Deep Learning in Medical Image Analysis (DLMIA), MICCAI 201

Comparative Susceptibility of Different Cell Cultures and Chicken Embryo Organ Cultures to Infectious Bursal Disease Virus of Poultry

Infectious bursal disease (IBD) is an acute highly contagious viral infection of young chickens often resulting in immunosuppression. Inactivated vaccines play significant role in protection against IBD. Mammalian cell lines could be used for producing such vaccines. In present study twenty-five, local strains of IBD virus were inoculated into chicken embryo bursa cell culture, liver cell culture, kidney cell culture, fibroblast cell culture and Vero cell lines for cytopathic effect. Moreover comparative susceptibility of chicken embryo bursa organ, embryo liver organ and embryo kidney organ cultures, to infectious bursal disease virus were studied. Chicken embryo bursa cell line was found to be most susceptible (90%) followed by Vero cell lines (70%), fibroblast cell lines (65%), kidney cell lines (50%) and liver cell lines (45%). While chicken embryo bursa organ culture gave maximum cytopathic effect (80%) followed by chicken embryo liver (60%) and kidney organ (45%). From these studies it is concluded that after bursa cell lines, Vero cell lines gave maximum cytopathic effect yielding high number of virus particles and are easy to maintain. Thus Vero cell lines can be used to produce infectious bursal disease vaccines using local isolates

Local structure prediction for gland segmentation

We present a method to segment individual glands from colon histopathology images. Segmentation based on sliding window classification does not usually make explicit use of information about the spatial configurations of class labels. To improve on this we propose to segment glands using a structure learning approach in which the local label configurations (structures) are considered when training a support vector machine classifier. The proposed method not only distinguishes foreground from background, it also distinguishes between different local structures in pixel labelling, e.g. locations between adjacent glands and locations far from glands. It directly predicts these label configurations at test time. Experiments demonstrate that it produces better segmentations than when the local label structure is not used to train the classifier

Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks

We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. For HC-SVM we further observe that training a second-level window classifier on the posterior probabilities - as an output refinement - can substantially improve the segmentation performance. The final performance of HC-SVM with refinement is comparable to that of CNN. Furthermore, we show that by combining and refining the posterior probability outputs of CNN and HC-SVM together, a further performance boost is obtained

Comparison Of ALT In Type 2 Diabetics with And Without Fatty Liver Disease

Objective: To determine the association of alanine aminotransferase in type 2 diabetic patients with and without fatty liver disease. Materials and Methods: A cross-sectional study was done for six months at Sheikh Khalifa Bin Zaid Al-Nahyan Hospital Rawalakot. In our study, we included all the patients who presented to the outpatient department (OPD) having type 2 diabetes mellitus. Their age, gender, height, weight, and duration of diabetes mellitus were noted. Their liver function test (LFTS), fasting blood sugar and HBA1c, and fasting lipid profile were also done at the time of their OPD visit and results were noted. They were given an appointment for an ultrasound abdomen from the radiology department for detection of fatty liver disease and the results were noted on the next OPD visit. Results: Total study population was 90 patients and out of which 35 (38.8%) were male and 55 (61.1%) were female. 58 years was the mean age of our study population. Fatty liver was present in 50% of patients. ALT was raised from a baseline value of 36 in 61% of patients while fasting blood sugar was raised in 83% of patients. The mean fasting blood sugar was 208 mg/dl. ALT was not significantly different in patients with and without fatty liver disease. However, it was found that patients with uncontrolled blood glucose levels have significantly raised ALT which was also statistically proven as the P value was less than 0.05. Also, patients with high blood glucose have a higher incidence of fatty liver disease as compared with normal blood glucose level patients but the difference was not statistically significant as shown by a P value more than 0.05. Conclusion: Fatty liver disease is more common in Type 2 diabetic patients with uncontrolled blood sugar. There is a high rate of raised ALT in diabetic patients whose blood sugar control is not optimum

An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens

AbstractImmunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test data sets were 87.1% and 88.5% for cell and specimen classification respectively. These were the highest achieved in the competition, suggesting that our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

BACKGROUND: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review. METHODS: In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software. RESULTS: Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; [Image: see text]=0.91, Quickscore; [Image: see text]=0.92). CONCLUSIONS: The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials