Conference Papers
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Item An Integrated Deep Learning Approach towards Automatic Evaluation of Ki-67 Labeling Index(Institute of Electrical and Electronics Engineers Inc., 2019) Lakshmi, S.; Vijayasenan, D.; Sumam David, S.; Sreeram, S.; Suresh, P.K.Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score. © 2019 IEEE.Item Deep Learning Model based Ki-67 Index estimation with Automatically Labelled Data(Institute of Electrical and Electronics Engineers Inc., 2020) Lakshmi, S.; Sai Ritwik, K.V.; Vijayasenan, D.; Sumam David, S.; Sreeram, S.; Suresh, P.K.Ki-67 labelling index is a biomarker which is used across the world to predict the aggressiveness of cancer. To compute the Ki-67 index, pathologists normally count the tumour nuclei from the slide images manually; hence it is timeconsuming and is subject to inter pathologist variability. With the development of image processing and machine learning, many methods have been introduced for automatic Ki-67 estimation. But most of them require manual annotations and are restricted to one type of cancer. In this work, we propose a pooled Otsu's method to generate labels and train a semantic segmentation deep neural network (DNN). The output is postprocessed to find the Ki-67 index. Evaluation of two different types of cancer (bladder and breast cancer) results in a mean absolute error of 3.52%. The performance of the DNN trained with automatic labels is better than DNN trained with ground truth by an absolute value of 1.25%. © 2020 IEEE.Item A Deep Learning Model for the Automatic Detection of Malignancy in Effusion Cytology(Institute of Electrical and Electronics Engineers Inc., 2020) Aboobacker, S.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.The excessive accumulation of fluid between layers of pleura covering lungs is known as pleural effusion. Pleural effusion may be due to various infections, inflammations or malignancy. The cytologists visually examine the microscopic slide to detect the malignant cells. The process is time-consuming, and interpretation of reactive cells and cells with ambiguous levels of atypia may differ between pathologists. Considerable research is happening towards the automation of fluid cytology reporting. We propose an integrated approach based on deep learning, where the network learns directly to detect the malignant cells in effusion cytology images. Architecture U-Net is used to learn the malignant and benign cells from the images and to detect the images that contain malignant cells. The model gives a precision of 0.96, recall of 0.96, and specificity of 0.97. The AUC of the ROC curve is 0.97. The model can be used as a screening tool and has a malignant cell detection rate of 0.96 with a low false alarm rate of 0.03. © 2020 IEEE.Item Semantic Segmentation on Low Resolution Cytology Images of Pleural and Peritoneal Effusion(Institute of Electrical and Electronics Engineers Inc., 2022) Aboobacker, S.; Verma, A.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.Automation in the detection of malignancy in effusion cytology helps to save time and workload for cytopathologists. Cytopathologists typically consider a low-resolution image to identify the malignant regions. The identified regions are scanned at a higher resolution to confirm malignancy by investigating the cell level behaviour. Scanning and processing time can be saved by zooming only the identified malignant regions instead of entire low-resolution images. This work predicts malignancy in cytology images at a very low resolution (4X). Annotation of cytology images at a very low resolution is challenging due to the blurring of features such as nuclei and texture. We address this issue by upsampling the very low-resolution images using adversarial training. This work develops a semantic segmentation model trained on 10X images and reuse the network to utilize the 4X images. The prediction results of low resolution images improved by 15% in average F-score for adversarial based upsampling compared to a bicubic filter. The high resolution model gives a 95% average F-score for high resolution images. Also, the sub-area of the whole slide that requires to be scanned at high magnification is reduced by approximately 61% while using adversarial based upsampling compared to a bicubic filter. © 2022 IEEE.Item Semi-supervised Semantic Segmentation of Effusion Cytology Images Using Adversarial Training(Springer Science and Business Media Deutschland GmbH, 2023) Rajpurohit, M.; Aboobacker, S.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.In pleural effusion, an excessive amount of fluid gets accumulated inside the pleural cavity along with signs of inflammation, infections, malignancies, etc. Usually, a manual cytological test is performed to detect and diagnose pleural effusion. The deep learning solutions for effusion cytology include a fully supervised model trained on effusion cytology images with the help of output maps. The low-resolution cytology images are harder to label and require the supervision of an expert, the labeling process time-consuming and expensive. Therefore, we have tried to use some portion of data without any labels for training our models using the proposed semi-supervised training methodology. In this paper, we proposed an adversarial network-based semi-supervised image segmentation approach to automate effusion cytology. The semi-supervised methodology with U-Net as the generator shows nearly 12% of absolute improvement in the f-score of benign class, 8% improvement in the f-score of malignant class, and 5% improvement in mIoU score as compared to a fully supervised U-Net model. With ResUNet++ as a generator, a similar improvement in the f-score of 1% for benign class, 8% for the malignant class, and 1% in the mIoU score is observed as compared to a fully supervised ResUNet++ model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Semi-supervised Semantic Segmentation for Effusion Cytology Images(Springer Science and Business Media Deutschland GmbH, 2023) Aboobacker, S.; Vijayasenan, D.; Sumam David, S.; Suresh, P.K.; Sreeram, S.Cytopathologists analyse images captured at different magnifications to detect the malignancies in effusions. They identify the malignant cell clusters from the lower magnification, and the identified area is zoomed in to study cell level details in high magnification. The automatic segmentation of low magnification images saves scanning time and storage requirements. This work predicts the malignancy in the effusion cytology images at low magnification levels such as 10 × and 4 ×. However, the biggest challenge is the difficulty in annotating the low magnification images, especially the 4 × data. We extend a semi-supervised learning (SSL) semantic model to train unlabelled 4 × data with the labelled 10 × data. The benign F-score on the predictions of 4 × data using the SSL model is improved 15% compared with the predictions of 4 × data on the semantic 10 × model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.