Eric Cosatto NEC Labs America

Eric Cosatto

Senior Researcher

Machine Learning

Posts

Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs (ECCB)

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Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.

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Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs

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Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images, provides a scalable alternative approach to decoding tissue complexity

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Improving Test-Time Adaptation For Histopathology Image Segmentation: Gradient-To-Parameter Ratio Guided Feature Alignment

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In the field of histopathology, computer-aided systems face significant challenges due to diverse domain shifts. They include variations in tissue source organ, preparation and scanningprotocols. These domain shifts can significantly impact algorithms’ performance in histopathology tasks, such as cancer segmentation. In this paper, we address this problem byproposing a new multi-task extension of test-time adaptation (TTA) for simultaneous semantic, and instance segmentation of nuclei. First, to mitigate domain shifts during testing, weuse a feature alignment TTA method, through which we adapt the feature vectors of the target data based on the feature vectors’ statistics derived from the source data. Second, the ratioof Gradient norm to Parameter norm (G2P) is proposed to guide the feature alignment procedure. Our approach requires a pre-trained model on the source data, without requiringaccess to the source dataset during TTA. This is particularly crucial in medical applications where access to training data may be restricted due to privacy concerns or patient consent. Through experimental validation, we demonstrate that the proposed method consistently yields competitive results when applied to out-of-distribution data across multiple datasets.

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Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists’ Visual Estimates

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The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurategenetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from hematoxylin and eosin (H&E)-stained images has been found to vary between pathologists, making it an important challenge to obtain an accurate TCR. In this study, three pathologists exhaustively labeled all cells in 41 regions from 41 lung cancer cases as either tumor, non-tumor or indistinguishable, thus establishing a “gold standard” TCR. We then compared the accuracy of the TCR estimated by 13 pathologists based on visual assessment and the TCR calculated by an AI model that we have developed. It is a compact and fast model that follows a fully convolutional neural network architecture and produces cell detection maps which can be efficiently post-processed to obtain tumor and non-tumor cell counts from which TCR is calculated. Its raw cell detection accuracy is 92% while its classification accuracy is 84%. The results show that the error between the gold standard TCR and the AI calculation was significantly smaller than that between the gold standard TCR and the pathologist’s visual assessment (p < 0.05). Additionally, the robustness of AI models across institutions is a key issue and we demonstrate that the variation in AI was smallerthan that in the average of pathologists when evaluated by institution. These findings suggest that the accuracy of tumor cellularity assessments in clinical workflows is significantly improved by the introduction of robust AI models, leading to more efficient genetic testing and ultimately to better patient outcomes.

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Real-time ConcealedWeapon Detection on 3D Radar Images forWalk-through Screening System

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This paper presents a framework for real-time concealed weapon detection (CWD) on 3D radar images for walk-through screening systems. The walk-through screening system aims to ensure security in crowded areas by performing CWD on walking persons, hence it requires an accurate and real-time detection approach. To ensure accuracy, a weapon needs to be detected irrespective of its 3D orientation, thus we use the 3D radar images as detection input. For achieving real-time, we reformulate classic U-Net based segmentation networks to perform 3D detection tasks. Our 3D segmentation network predicts peak-shaped probability map, instead of voxel-wise masks, to enable position inference by elementary peak detection operation on the predicted map. In the peak-shaped probability map, the peak marks the weapon’s position. So, weapon detection task translates to peak detection on the probability map. A Gaussian function is used to model weapons in the probability map. We experimentally validate our approach on realistic 3D radar images obtained from a walk-through weapon screening system prototype. Extensive ablation studies verify the effectiveness of our proposed approach over existing conventional approaches. The experimental results demonstrate that our proposed approach can perform accurate and real-time CWD, thus making it suitable for practical applications of walk-through screening.

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Prediction of Non-Muscle Invasive Bladder Cancer Recurrence using Machine Learning of Quantitative Nuclear Features

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Non-muscle invasive bladder cancer (NMIBC) generally has a good prognosis, however, recurrence after transurethral resection (TUR), the standard primary treatment, is a major problem. Clinical management after TUR has been based on risk classification using clinicopathological factors, but these classifications are not complete. In this study, we attempted to predict early recurrence of NMIBC based on machine learning of quantitative morphological features. In general, structural, cellular, and nuclear atypia are evaluated to determine cancer atypia. However, since it is difficult to accurately quantify structural atypia from TUR specimens, in this study, we used only nuclear atypia and analyzed it using feature extraction followed by classification using Support Vector Machine and Random Forest machine learning algorithms. For the analysis, 125 patients diagnosed with NMIBC were used, data from 95 patients were randomly selected for the training set, and data from 30 patients were randomly selected for the test set. The results showed that the support vector machine-based model predicted recurrence within 2 years after TUR with a probability of 90% and the random forest-based model with probability of 86.7%. In the future, the system can be used to objectively predict NMIBC recurrence after TUR.

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Automatic Fine-Grained Localization of Utility Pole Landmarks on Distributed Acoustic Sensing Traces Based on Bilinear Resnets

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In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.

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A Multi-Scale Conditional Deep Model for Tumor Cell Ratio Counting

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We propose a method to accurately obtain the ratio of tumor cells over an entire histological slide. We use deep fully convolutional neural network models trained to detect and classify cells on images of H&E-stained tissue sections. Pathologists’ labels consisting of exhaustive nuclei locations and tumor regions were used to trained the model in a supervised fashion. We show that combining two models, each working at a different magnification allows the system to capture both cell-level details and surrounding context to enable successful detection and classification of cells as either tumor-cell or normal-cell. Indeed, by conditioning the classification of a single cell on a multi-scale context information, our models mimic the process used by pathologists who assess cell neoplasticity and tumor extent at different microscope magnifications. The ratio of tumor cells can then be readily obtained by counting the number of cells in each class. To analyze an entire slide, we split it into multiple tiles that can be processed in parallel. The overall tumor cell ratio can then be aggregated. We perform experiments on a dataset of 100 slides with lung tumor specimens from both resection and tissue micro-array (TMA). We train fully-convolutional models using heavy data augmentation and batch normalization. On an unseen test set, we obtain an average mean absolute error on predicting the tumor cell ratio of less than 6%, which is significantly better than the human average of 20% and is key in properly selecting tissue samples for recent genetic panel tests geared at prescribing targeted cancer drugs. We perform ablation studies to show the importance of training two models at different magnifications and to justify the choice of some parameters, such as the size of the receptive field.

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Prediction of Early Recurrence of Hepatocellular Carcinoma after Resection using Digital Pathology Images Assessed by Machine Learning

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Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test), Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28), and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation, however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.

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Size and Alignment Independent Classification of the High-order Spatial Modes of a Light Beam Using a Convolutional Neural Network

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The higher-order spatial modes of a light beam are receiving significant interest. They can be used to further increase the data speeds of high speed optical communication, and for novel optical sensing modalities. As such, the classification of higher-order spatial modes is ubiquitous. Canonical classification methods typically require the use of unconventional optical devices. However, in addition to having prohibitive cost, complexity, and efficacy, such methods are dependent on the light beam’s size and alignment. In this work, a novel method to classify higher-order spatial modes is presented, where a convolutional neural network is applied to images of higher-order spatial modes that are taken with a conventional camera. In contrast to previous methods, by training the convolutional neural network with higher-order spatial modes of various alignments and sizes, this method is not dependent on the light beam’s size and alignment. As a proof of principle, images of 4 Hermite-Gaussian modes (HG00, HG01, HG10, and HG11) are numerically calculated via known solutions to the electromagnetic wave equation, and used to synthesize training examples. It is shown that as compared to training the convolutional neural network with training examples that have the same sizes and alignments, a?~2×?increase in accuracy can be achieved.

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