Deep learning for magnification independent breast cancer histopathology image classification

Abstract Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data

Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

Abstract Objective: To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting. Results: Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714). Conclusions: We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA

Towards virtual H&E staining of hyperspectral lung histology images using conditional generative adversarial networks

Abstract The microscopic image of a specimen in the absence of staining appears colorless and textureless. Therefore, microscopic inspection of tissue requires chemical staining to create contrast. Hematoxylin and eosin (H&E) is the most widely used chemical staining technique in histopathology. However, such staining creates obstacles for automated image analysis systems. Due to different chemical formulations, different scanners, section thickness, and lab protocols, similar tissues can greatly differ in appearance. This huge variability is one of the main challenges in designing robust and resilient automated image analysis systems. Moreover, staining process is time consuming and its chemical effects deform structures of specimens. In this work, we develop a method to virtually stain unstained specimens. Our method utilizes dimension reduction and conditional adversarial generative networks (cGANs) which build highly non-linear mappings between input and output images. Conditional GANs ability to handle very complex functions and high dimensional data enables transforming unstained hyperspectral tissue image to their H&E equivalent which comprises highly diversified appearance. In the long term, such virtual digital H&E staining could automate some of the tasks in the diagnostic pathology workflow which could be used to speed up the sample processing time, reduce costs, prevent adverse effects of chemical stains on tissue specimens, reduce observer variability, and increase objectivity in disease diagnosis