Papers by Harshita Sharma
Determining similarity in histological images using graph-theoretic description and matching meth... more Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics
Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, 2020
Lecture Notes in Computer Science, 2019
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020
Ultrasound in Obstetrics & Gynecology, 2020
2022 Symposium on Eye Tracking Research and Applications
2022 Symposium on Eye Tracking Research and Applications
2022 Symposium on Eye Tracking Research and Applications
2022 Symposium on Eye Tracking Research and Applications
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018
We present a novel automated approach for detection of standardized abdominal circumference (AC) ... more We present a novel automated approach for detection of standardized abdominal circumference (AC) planes in fetal ultrasound built in a convolutional neural network (CNN) framework, called SonoEyeNet, that utilizes eye movement data of a sonographer in automatic interpretation. Eye movement data was collected from experienced sonographers as they identified an AC plane in fetal ultrasound video clips. A visual heatmap was generated from the eye movements for each video frame. A CNN model was built using ultrasound frames and their corresponding visual heatmaps. Different methods of processing visual heatmaps and their fusion with image feature maps were investigated. We show that with the assistance of human visual fixation information, the precision, recall and F1-score of AC plane detection was increased to 96.5%, 99.0% and 97.8% respectively, compared to 73.6%, 74.1% and 73.8% without using eye fixation information.
Medical Image Analysis, 2021
Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly op... more Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to the vast amounts of data. Such research would be informative to better understand operator clinical workflow when conducting ultrasound scans to support skills training, optimise scan times, and inform building better user-machine interfaces. This paper is to our knowledge the first to address sonography data science, which we consider in the context of second-trimester fetal sonography screening. Specifically, we present a fully-automatic framework to analyse operator clinical workflow solely from full-length routine second-trimester fetal ultrasound scan videos. An ultrasound video dataset containing more than 200 hours of scan recordings was generated for this study. We developed an original deep learning method to temporally segment the ultrasound video into semantically meaningful segments (the video description). The resulting semantic annotation was then used to depict operator clinical workflow (the knowledge representation). Machine learning was applied to the knowledge representation to characterise operator skills and assess operator variability. For video description, our best-performing deep spatio-temporal network shows favourable results in cross-validation (accuracy: 91.7%), statistical analysis (correlation: 0.98, p < 0.05) and retrospective manual validation (accuracy: 76.4%). For knowledge representation of operator clinical workflow, a three-level abstraction scheme consisting of a Subject-specific Timeline Model (STM), Summary of Timeline Features (STF), and an Operator Graph Model (OGM), was introduced that led to a significant decrease in dimensionality and computational complexity compared to raw video data. The workflow representations were learnt to discriminate between operator skills, where a proposed convolutional neural network-based model showed most promising performance (cross-validation accuracy: 98.5%, accuracy on unseen operators: 76.9%). These were further used to derive operator-specific scanning signatures and operator variability in terms of type, order and time distribution of constituent tasks.
Diagnostic Pathology, 2016
Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma... more Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma are analyzed using deep learning methods. A convolutional neural network architecture is proposed for two classification applications in H&E stained tissue images, namely, cancer classification based on immunohistochemistry (IHC) into classes Her2/neu+ tumor, Her2/neu- tumor and non-tumor, and necrosis detection based on existence of necrosis into classes necrotic and non-necrotic. The studies in [1] and [2] explored computer-aided classification using graphbased methods and necrosis detection by textural approach respectively, which are extended using deep convolutional neural networks. Performance is quantitatively compared with established handcrafted image features, namely Haralick GLCM, Gabor filter-banks, LBP histograms, Gray histograms, RGB histograms and HSV histograms followed by classification by random forests, another well-known machine learning algorithm. Aims Convolutional...
2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 2017
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 2018
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
Lecture Notes in Computer Science, 2019
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019
IEEE Transactions on Cybernetics, 2020
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Papers by Harshita Sharma