Browsing by Subject "Breast histopathology"
Now showing 1 - 11 of 11
- Results Per Page
- Sort Options
Item Open Access Deep feature representations and multi-instance multi-label learning of whole slide breast histopathology images(2019-03) Mercan, CanerThe examination of a tissue sample has traditionally involved a pathologist investigating the case under a microscope. Whole slide imaging technology has recently been utilized for the digitization of biopsy slides, replicating the microscopic examination procedure with the computer screen. This technology made it possible to scan the slides at very high resolutions, reaching up to 100; 000 100; 000 pixels. The advancements in the imaging technology has allowed the development of automated tools that could help reduce the workload of pathologists during the diagnostic process by performing analysis on the whole slide histopathology images. One of the challenges of whole slide image analysis is the ambiguity of the correspondence between the diagnostically relevant regions in a slide and the slide-level diagnostic labels in the pathology forms provided by the pathologists. Another challenge is the lack of feature representation methods for the variable number of variable-sized regions of interest (ROIs) in breast histopathology images as the state-of-the-art deep convolutional networks can only operate on fixed-sized small patches which may cause structural and contextual information loss. The last and arguably the most important challenge involves the clinical significance of breast histopathology, for the misdiagnosis or the missed diagnoses of a case may lead to unnecessary surgery, radiation or hormonal therapy. We address these challenges with the following contributions. The first contribution introduces the formulation of the whole slide breast histopathology image analysis problem as a multi-instance multi-label learning (MIMLL) task where a slide corresponds to a bag that is associated with the slide-level diagnoses provided by the pathologists, and the ROIs inside the slide correspond to the instances in the bag. The second contribution involves a novel feature representation method for the variable number of variable-sized ROIs using the activations of deep convolutional networks. Our final contribution includes a more advanced MIMLL formulation that can simultaneously perform multi-class slide-level classification and ROI-level inference. Through quantitative and qualitative experiments, we show that the proposed MIMLL methods are capable of learning from only slide-level information for the multi-class classification of whole slide breast histopathology images and the novel deep feature representations outperform the traditional features in fully supervised and weakly supervised settings.Item Open Access Deep feature representations for variable-sized regions of ınterest in breast histopathology(IEEE, 2021) Mercan, Caner; Aygüneş, Bulut; Aksoy, Selim; Mercan, Ezgi; Shapiro, L. G.; Weaver, D. L.; Elmore, J. G.Objective: Modeling 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. Methods: First, 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. Results: Experiments 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. Conclusion: The 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. Significance: The 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.Item Open Access Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks(Elsevier, 2018) Geçer, Barış; Aksoy, Selim; Mercan, E.; Shapiro, L. G.; Weaver, D. L.; Elmore, J. G.Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for clinically more significant multi-class scenarios where intermediate categories have different risk factors and treatment strategies. We present a system that classifies whole slide images (WSI) of breast biopsies into five diagnostic categories. First, a saliency detector that uses a pipeline of four fully convolutional networks, trained with samples from records of pathologists’ screenings, performs multi-scale localization of diagnostically relevant regions of interest in WSI. Then, a convolutional network, trained from consensus-derived reference samples, classifies image patches as non-proliferative or proliferative changes, atypical ductal hyperplasia, ductal carcinoma in situ, and invasive carcinoma. Finally, the saliency and classification maps are fused for pixel-wise labeling and slide-level categorization. Experiments using 240 WSI showed that both saliency detector and classifier networks performed better than competing algorithms, and the five-class slide-level accuracy of 55% was not statistically different from the predictions of 45 pathologists. We also present example visualizations of the learned representations for breast cancer diagnosis.Item Open Access From patch-level to ROI-level deep feature representations for breast histopathology classification(SPIE, 2019) Mercan, Caner; Aksoy, Selim; Mercan, E.; Shapiro, L. G.; Weaver, D. L.; Elmore, J. G.; Tomaszewski, J. E.; Ward, A. D.We propose a framework for learning feature representations for variable-sized regions of interest (ROIs) in breast histopathology images from the convolutional network properties at patch-level. The proposed method involves fine-tuning a pre-trained convolutional neural network (CNN) by using small fixed-sized patches sampled from the ROIs. The CNN is then used to extract a convolutional feature vector for each patch. The softmax probabilities of a patch, also obtained from the CNN, are used as weights that are separately applied to the feature vector of the patch. The final feature representation of a patch is the concatenation of the class-probability weighted convolutional feature vectors. Finally, the feature representation of the ROI is computed by average pooling of the feature representations of its associated patches. The feature representation of the ROI contains local information from the feature representations of its patches while encoding cues from the class distribution of the patch classification outputs. The experiments show the discriminative power of this representation in a 4-class ROI-level classification task on breast histopathology slides where our method achieved an accuracy of 66.8% on a data set containing 437 ROIs with different sizes.Item Open Access Graph convolutional networks for region of interest classification in breast histopathology(S P I E - International Society for Optical Engineering, 2021) Aygüneş, Bulut; Aksoy, Selim; Cinbiş, R.G.; Kösemehmetoğlu, K.; Önder, S.; Üner, A.Deep learning-based approaches have shown highly successful performance in the categorization of digitized biopsy samples. The commonly used setting in these approaches is to employ convolutional neural networks for classification of data sets consisting of images all having the same size. However, the clinical practice in breast histopathology necessitates multi-class categorization of regions of interest (ROI) in biopsy samples where these regions can have arbitrary shapes and sizes. The typical solution to this problem is to aggregate the classification results of fixed-sized patches cropped from these images to obtain image-level classification scores. Another limitation of these approaches is the independent processing of individual patches where the rich contextual information in the complex tissue structures has not yet been sufficiently exploited. We propose a generic methodology to incorporate local inter-patch context through a graph convolution network (GCN) that admits a graph-based ROI representation. The proposed GCN model aims to propagate information over neighboring patches in a progressive manner towards classifying the whole ROI into a diagnostic class. The experiments using a challenging data set for a 4-class ROI-level classification task and comparisons with several baseline approaches show that the proposed model that incorporates the spatial context by using graph convolutional layers performs better than commonly used fusion rules.Item Open Access Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images(Institute of Electrical and Electronics Engineers, 2018) Mercan, C.; Aksoy, Selim; Mercan, E.; Shapiro, L. G.; Weaver, D. L.; Elmore, J. G.Digital pathology has entered a new era with the availability of whole slide scanners that create the high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significances have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROIs) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists' image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories.Item Open Access Multi-instance multi-label learning for whole slide breast histopathology(International Society for Optical Engineering SPIE, 2016-02-03) Mercan, Caner; Mercan, E.; Aksoy, Selim; Shapiro, L. G.; Weaver, D. L.; Elmore, J. G.Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis systems. However, the whole slide images also provide two new challenges to image analysis algorithms. The first one is the need for simultaneous localization and classification of malignant areas in these large images, as different parts of the image may have different levels of diagnostic relevance. The second challenge is the uncertainty regarding the correspondence between the particular image areas and the diagnostic labels typically provided by the pathologists at the slide level. In this paper, we exploit a data set that consists of recorded actions of pathologists while they were interpreting whole slide images of breast biopsies to find solutions to these challenges. First, we extract candidate regions of interest (ROI) from the logs of pathologists' image screenings based on different actions corresponding to zoom events, panning motions, and fixations. Then, we model these ROIs using color and texture features. Next, we represent each slide as a bag of instances corresponding to the collection of candidate ROIs and a set of slide-level labels extracted from the forms that the pathologists filled out according to what they saw during their screenings. Finally, we build classifiers using five different multi-instance multi-label learning algorithms, and evaluate their performances under different learning and validation scenarios involving various combinations of data from three expert pathologists. Experiments that compared the slide-level predictions of the classifiers with the reference data showed average precision values up to 62% when the training and validation data came from the same individual pathologist's viewing logs, and an average precision of 64% was obtained when the candidate ROIs and the labels from all pathologists were combined for each slide. © 2016 SPIE.Item Open Access On the benefits of region of interest detection for whole slide image classification(SPIE, 2023-04-06) Korkut, Sena; Erkan, Cihan; Aksoy, Selim; Tomaszewski, John E.; Ward, Aaron D.Whole slide image (WSI) classification methods typically use fixed-size patches that are processed separately and are aggregated for the final slide-level prediction. Image segmentation methods are designed to obtain a delineation of specific tissue types. These two tasks are usually studied independently. The aim of this work is to investigate the effect of region of interest (ROI) detection as a preliminary step for WSI classification. First, we process each WSI by using a pixel-level classifier that provides a binary segmentation mask for potentially important ROIs. We evaluate both single-resolution models that process each magnification independently and multi-resolution models that simultaneously incorporate contextual information and local details. Then, we compare the WSI classification performances of patch-based models when the patches used for both training and testing are extracted from the whole image and when they are sampled from only within the detected ROIs. The experiments using a binary classification setting for breast histopathology slides as benign vs. malignant show that the classifier that uses the patches sampled from the whole image achieves an F1 score of 0.68 whereas the classifiers that use patches sampled from the ROI detection results produced by the single- and multi-resolution models obtain scores between 0.75 and 0.83.Item Open Access Self-supervised representation learning with graph neural networks for region of interest analysis in breast histopathology(2020-12) Özen, YiğitDeep learning has made a major contribution to histopathology image analysis with representation learning outperforming hand-crafted features. However, two notable challenges remain. The first is the lack of large histopathology datasets. The commonly used setting in deep learning-based approaches is supervised training of deep and wide models using large labeled datasets. Manually labeling histopathology images is a time-consuming operation. Assembling a large public dataset is also proven difficult due to privacy concerns. Second, the clinical practice in histopathology necessitates working with regions of interest of multiple diagnostic classes with arbitrary shapes and sizes. The typical solution to this problem is to aggregate the representations of fixed-sized patches cropped from these regions to obtain region-level representations. However, naive methods cannot sufficiently exploit the rich contextual information in the complex tissue structures. To tackle these two challenges, this thesis proposes a generic method that utilizes graph neural networks, combined with a self-supervised training method using a contrastive loss function. The regions of interest are modeled as graphs where vertices are fixed-sized patches cropped from the region. The proposed method has two stages. The first stage is patch-level representation learning using convolutional neural networks which concentrates on cell-level features. The second stage is region-level representation learning using graph neural networks using vertex dropout augmentation. The experiments using a challenging breast histopathology dataset show that the proposed method achieves better performance than the state-of-the-art in both classification and retrieval tasks. networks which can learn the tissue structure. Graph neural networks enable representing arbitrarily-shaped regions as graphs and encoding contextual information through message passing between neighboring patches. Self-supervised contrastive learning improves quality of learned representations without requiring labeled data. We propose using self-supervised learning to train graph neural networks using vertex dropout augmentation. The experiments using a challenging breast histopathology dataset show that the proposed method achieves better performance than the state-of-the-art in both classification and retrieval tasks.Item Open Access Use of subgraph mining in histopathology image classification(2022-09) Berdiyev, BayramBreast cancer is the most common cancer in women and has a high mortality rate. Computer vision techniques can be used to help experts to analyze the breast cancer biopsy samples better. Graph neural networks (GNN) have been widely used to solve the classification of breast cancer images. Images in this field have varying sizes and GNNs can be applied to varying sized inputs. Graphs can store relations between the vertices of the graph and this is another reason why GNNs are preferred as a solution. We study the use of subgraph mining in classification of regions of interest (ROI) on breast histopathology images. We represent ROI samples with graphs by using patches sampled on nuclei-rich regions as the vertices of the graph. Both micro and macro level information are essential when classifying histopathology images. The patches are used to model micro-level information. We apply subgraph mining to the resulting graphs to identify frequently occurring subgraphs. Each subgraph is composed of a small number of patches and their relations, which can be used to represent higher level information. We also extract ROI-level features by applying a sliding window mechanism with larger sized patches. The ROI-level features, subgraph features and a third representation obtained from graph convolutional networks are fused to model macro-level information about the ROIs. We also study embedding the subgraphs in the graph representation as additional vertices. The proposed models are evaluated on a challenging breast pathology dataset that includes four diagnostic categories from the full spectrum. The experiments show that embedding the subgraphs in the graph representation improves the classification accuracy and the fused feature representation performs better than the individual representations in an ablation study.Item Open Access Weakly supervised approaches for image classification in remote sensing and medical image analysis(2020-12) Aygüneş, BulutWeakly supervised learning (WSL) aims to utilize data with imprecise or noisy annotations to solve various learning problems. We study WSL approaches in two different domains: remote sensing and medical image analysis. For remote sensing, we focus on the multisource fine-grained object recognition problem that aims to classify an object into one of many similar subcategories. The task we work on involves images where an object with a given class label is present in the image without any knowledge of its exact location. We approach this problem from a WSL perspective and propose a method using a single-source deep instance attention model with parallel branches for joint localization and classification of objects. We then extend this model into a multisource setting where a reference source assumed to have no location uncertainty is used to aid the fusion of multiple sources. We show that all four proposed fusion strategies that operate at the probability level, logit level, feature level, and pixel level provide higher accuracies compared to the state-of-the-art. We also provide an in-depth comparison by evaluating each model at various parameter complexity settings, where the increased model capacity results in a further improvement over the default capacity setting. For medical image analysis, we study breast cancer classification on regions of interest (ROI) of arbitrary shapes and sizes from breast biopsy whole slides. The typical solution to this problem is to aggregate the classification results of fixed-sized patches cropped from ROIs to obtain image-level classification scores. We first propose a generic methodology to incorporate local inter-patch context through a graph convolution network (GCN) that aims to propagate information over neighboring patches in a progressive manner towards classifying the whole ROI. The experiments using a challenging data set for a 3-class ROI-level classification task and comparisons with several baseline approaches show that the proposed model that incorporates the spatial context performs better than commonly used fusion rules. Secondly, we revisit the WSL framework we use in our remote sensing experiments and apply it to a 4-class ROI classification problem. We propose a new training methodology tailored for this WSL task that combines the patches and labels from pairs of ROIs together to exploit the instance attention model’s capability to learn from samples with multiple labels, which results in superior performance over several baselines.