Publications
Showing 88 results for Author: Georgia Tourassi
Dec, 2023
Conference Paper
Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks
Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we tr…
Dec, 2023
Journal
Limitations of Transformers on Clinical Text Classification
Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language processing (NLP) tasks; however, their application to document classification on long clinical texts is limited. In this work, we introduce four methods to scale BERT, which by default can only handle input sequences up to appro…
Dec, 2023
Journal
Privacy-Preserving Deep Learning NLP Models for Cancer Registries
Population cancer registries can benefit from Deep Learning (DL) to automatically extract cancer characteristics from the high volume of unstructured pathology text reports they process annually. The success of DL to tackle this and other real-world problems is proportional to the availability of large labeled datasets for model training. Although collaboration among cance…
Dec, 2023
Conference Paper
Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters
Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. % Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. % Processing a large volume of the pathology reports in a timely manner is…
Dec, 2023
Conference Paper
Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters
Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. Processing a large volume of the pathology reports in a timely manner is a c…
Dec, 2023
Conference Paper
Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports
Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitat…
Dec, 2023
Conference Paper
Visualization for Classification in Deep Neural Networks
Recently, the techniques based on Deep Neural Networks (DNNs) have achieved a great performance in classification tasks in a wide range of applications, such as image recognition and natural language processing. However, DNN developers face a lot of trial and error during the development process and spend their efforts in debugging their network model through analyzing and…
Nov, 2023
Conference Paper
Adversarial Training for Privacy-Preserving Deep Learning Model Distribution
Collaboration among cancer registries is essential to develop accurate, robust, and generalizable deep learning models for automated information extraction from cancer pathology reports. Sharing data presents a serious privacy issue, especially in biomedical research and healthcare delivery domains. Distributing pretrained deep learning (DL) models has been proposed to avo…
Nov, 2023
Conference Paper
Computer-aided detection using non-convolutional neural network Gaussian processes
Deep convolutional neural networks (CNNs) have in recent years achieved record-breaking performance on many image classification tasks and are therefore well-suited for computer aided detection (CAD). The need for uncertainty quantification for CAD motivates the need for a probabilistic framework for deep learning. The most well-known probabilistic neural network model is…
Nov, 2023
Journal
Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology
BACKGROUND: As artificial intelligence and machine learning techniques emerged in biomedical informatics, security and privacy concerns over the data and subject identities have also become an issue and essential research topic. The machine learning models are agnostic to the personal identity but find any patterns and features that improve task performance. OBJECTIVE: The…
Nov, 2023
Conference Paper
Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks
To trust model predictions, it is important to ensure new data scored by the model comes from the same population used for model training. If the model is used to score new data different than the model’s training data, then predictions and model performance metrics cannot be trusted. Identifying and excluding these anomalous data points is an important task when using mod…