Learning to Segment Breast Biopsy Whole Slide Images

We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks cannot be applied directly on large biopsy images and the different sized structures in biopsies present novel challenges, we propose four modifications: (1) an input-aware encoding block to compensate for information loss, (2) a new dense connection pattern between encoder and decoder, (3) dense and sparse decoders to combine multi-level features, (4) a multi-resolution network that fuses the results of encoder-decoders run on different resolutions. Our model outperforms a feature-based approach and conventional encoder-decoders from the literature. We use semantic segmentations produced with our model in an automated diagnosis task and obtain higher accuracies than a baseline approach that employs an SVM for feature-based segmentation, both using the same segmentation-based diagnostic features.Comment: Added more WSI images in appendi

A Randomized Study Comparing Digital Imaging to Traditional Glass Slide Microscopy for Breast Biopsy and Cancer Diagnosis.

BACKGROUND: Digital whole slide imaging may be useful for obtaining second opinions and is used in many countries. However, the U.S. Food and Drug Administration requires verification studies. METHODS: Pathologists were randomized to interpret one of four sets of breast biopsy cases during two phases, separated by ≥9 months, using glass slides or digital format (sixty cases per set, one slide per case, RESULTS: Sixty-five percent of responding pathologists were eligible, and 252 consented to randomization; 208 completed Phase I (115 glass, 93 digital); and 172 completed Phase II (86 glass, 86 digital). Accuracy was slightly higher using glass compared to digital format and varied by category: invasive carcinoma, 96% versus 93% ( CONCLUSIONS: In this large randomized study, digital format interpretations were similar to glass slide interpretations of benign and invasive cancer cases. However, cases in the middle of the spectrum, where more inherent variability exists, may be more problematic in digital format. Future studies evaluating the effect these findings exert on clinical practice and patient outcomes are required

Digital Pathology: Diagnostic Errors, Viewing Behavior and Image Characteristics

Thesis (Ph.D.)--University of Washington, 2017-06Whole slide imaging technologies provide a unique opportunity to collect and analyze large amounts of data on pathologists' interactions with the digital slide. In this work, we are studying the underlying causes of diagnostic errors in histopathology. Instead of focusing on the detection of invasive cancer, we consider the full-spectrum of diagnoses that a pathologist encounters during clinical practice and aim to study misidentification and misinterpretation errors that may cause overdiagnoses or underdiagnoses. To this end, we use the digiPATH dataset that consists of 240 breast biopsies with diagnoses ranging from benign to invasive cancer, the actions of pathologists recorded during their interpretations of the slides and the diagnostic regions associated with the final diagnoses they assigned. Our work consists of three parts: region of interest localization, diagnostic classification and viewing behavior analysis. The first part of our work introduces a novel methodology to extract the diagnostically relevant regions of interest from pathologists' viewing behavior, and a computer vision model to detect these regions automatically on unseen images. Region of interest (ROI) localization provides us with a set of regions on the whole slide that either leads to the correct diagnosis or distracts the pathologists. The largest portion of this thesis is devoted to the diagnostic classification problem. Starting with a tissue labeling, we developed features that describe the tissue composition of the image and the structural changes. We first introduce two models for the semantic segmentation of the regions of interest into tissue labels. Then, we define two different feature sets that are constructed from the tissue label images. The first feature set consists of superpixel-label frequency and co-occurrence histograms, which are common image features. The second set of features are a sequence of histograms that together comprise the structure feature, a new kind of image feature defined for the first time in this work. Instead of attempting a four-class classification (benign, atypia, DCIS and invasive), we classify images one diagnosis at a time starting with invasive versus benign and ending with atypia versus DCIS. We show that the superpixel-label frequency and co-occurrence histograms work best for the classification of the invasive cases while the structure feature is more suitable for the benign, atypia and DCIS cases. The final part is an analysis of the pathologists' behavior on the whole slide images. We first analyze the relationship between the identification of the correct ROI and the diagnosis. We show that the higher overlap with the consensus ROI is correlated with a higher diagnostic accuracy. Then, we introduce novel measurements of interpretative patterns and identify two strategies used by the pathologists: scanning and drilling. We demonstrate that the interpretation strategy does not change the diagnostic accuracy but drilling is the more efficient option. Although it does not affect the diagnostic outcome, the interpretation strategy is correlated with the pathologists' characteristics like gender, age, experience and nervousness

An Investigation of the Diving Tourism Potential of Çanakkale Province

anemonDalış turizmi, alternatif turizm türlerinden biri olup, Çanakkale’de de gerçekleştirilmektedir. Çalışma ile, Çanakkale’de dalış turizmi potansiyeli ortaya konulmaya çalışılmıştır. Çanakkale’nin dalış turizmi potansiyelini değerlendirmek için katılımcılarla yapılandırılmış görüşmeler gerçekleştirilmiştir. Böylelikle, turizm paydaşları için sürdürülebilir dalış turizmi hakkında öneriler getirilmiş, Çanakkale dalış turizmine ilişkin güçlü ve zayıf yönler ile fırsat ve tehditler ortaya konulmuştur. Çalışmada, Çanakkale’nin dalış turizmi potansiyelinin yüksek olduğu ancak birçok faktörün dalış turizminin gelişiminin önünde engeller oluşturduğu sonucuna ulaşılmıştır.Diving tourism is one of the alternative tourism types and it is also carried out in Çanakkale. With the study, it has been tried to reveal what the diving tourism potential is in Çanakkale. Structured interviews were conducted with the participants in order to evaluate the diving tourism potential of Çanakkale. Thus, suggestions were made for tourism stakeholders on sustainable diving tourism, and strengths, weaknesses, opportunities and threats regarding Çanakkale diving tourism were revealed. In the study, it was concluded that Çanakkale has a high potential for diving tourism, but many factors pose obstacles to the development of diving tourism.82178

Deep Feature Representations for Variable-Sized Regions of Interest in Breast Histopathology

ObjectiveModeling variable-sized regions of interest (ROIs) in whole slide images using deep convolutional networks is a challenging task, as these networks typically require fixed-sized inputs that should contain sufficient structural and contextual information for classification. We propose a deep feature extraction framework that builds an ROI-level feature representation via weighted aggregation of the representations of variable numbers of fixed-sized patches sampled from nuclei-dense regions in breast histopathology images.MethodsFirst, the initial patch-level feature representations are extracted from both fully-connected layer activations and pixel-level convolutional layer activations of a deep network, and the weights are obtained from the class predictions of the same network trained on patch samples. Then, the final patch-level feature representations are computed by concatenation of weighted instances of the extracted feature activations. Finally, the ROI-level representation is obtained by fusion of the patch-level representations by average pooling.ResultsExperiments using a well-characterized data set of 240 slides containing 437 ROIs marked by experienced pathologists with variable sizes and shapes result in an accuracy score of 72.65% in classifying ROIs into four diagnostic categories that cover the whole histologic spectrum.ConclusionThe results show that the proposed feature representations are superior to existing approaches and provide accuracies that are higher than the average accuracy of another set of pathologists.SignificanceThe proposed generic representation that can be extracted from any type of deep convolutional architecture combines the patch appearance information captured by the network activations and the diagnostic relevance predicted by the class-specific scoring of patches for effective modeling of variable-sized ROIs

The Use of Pseudo-landmarks for Craniofacial Analysis: A Comparative Study with L1-Regularized Logistic Regression

Abstract — Morphometrics, the quantitative analysis of shape, is used by craniofacial researchers to study abnormalities in human face shapes. Most of the work in craniofacial morphometrics uses landmark points that are manually marked on 3D face data and processed via a generalized Procrustes analysis. For large data sets this manual process is very time-consuming. Dense sets of pseudo-landmarks have also been proposed and successfully used for classification and clustering, but the two main methods in the literature are very computationally intensive. We have developed a computationally simple method that can compute pseudo-landmark points at different resolutions from 3D meshes of human faces. In this paper, we perform a comparative study employing L1-regularized logistic regression to train a classifier that predicts the sex of 500 normal adult face meshes in order to compare our method to two alternative pseudo-landmark methods and a distance matrix approach.Our results show that our method, which is fully automatic, achieved similar results to the best-scoring methods with no manual landmarking and with much lower computation time. Use of the distance matrix did not improve classification results. I

Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

ImportanceFollowing recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools.ObjectiveTo develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer.Design, setting, and participantsIn this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019.Main outcomes and measuresDiagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists.ResultsThe accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82).Conclusion and relevanceThe computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia