Faculty Publications
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Publications by NITK Faculty
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Item Automated hard exudate segmentation using neural encoders and attention mechanisms for diabetic retinopathy diagnosis(Inderscience Publishers, 2023) Gawas, P.; Sowmya Kamath, S.Diabetic retinopathy (DR) is a complication caused by increased blood glucose levels, which causes retinal damage in diabetic patients’ eyes. If not discovered and treated early, it can lead to vision loss. Hard exudates (HE) are one of its characteristic signs. Identification of HE is a paramount step in early diagnosis of DR. In this work, the suitability of U-Net-based deep CNN with different encoder configurations and attention gates (AG) is experimented, for HE segmentation. The proposed models were benchmarked on the standard IDRiD dataset. To overcome the challenges related to the limited dataset, data augmentation techniques were also applied to generate image patches and used for model training. Extensive experiments on the dataset revealed that U-Net with AG achieved an accuracy of 98.8%. The U-Net with ResNet50 as the encoder backbone achieved an accuracy of 98.64%. The findings show that the presented models are effective and suitable for early-stage clinical diagnosis. © © 2023 Inderscience Enterprises Ltd.Item Multistage Image Reconstruction and Attention-Based Semi-Supervised Learning for Medical Image Segmentation(SAGE Publications Ltd, 2025) Gawas, P.; Kamath S, S.; Singh, A.; Gurupur, V.Automated segmentation of medical images is critical in detecting and diagnosing various conditions. In recent years, supervised deep learning (DL) techniques have been widely researched. However, their application is often limited by the availability of annotated data in the medical domain. To address this, recent studies have explored semi-supervised techniques, though very few of these works focus on skin-lesion segmentation. In addition, they struggle to effectively capture contextual features to delineate the region of interest from the surrounding tissues in the image, which is crucial for accurate segmentation. In this article, a semi-supervised approach for medical image segmentation called MIRA (Medical Image Reconstruction and Analysis) is proposed, which uses adaptive-attention U-Net (AA-U-Net) trained on pseudo-labels generated with a lightweight feature-consistent encoder-decoder network (FCED-Net) to address these challenges. A case study focusing on the precise segmentation of malignant skin lesions is considered for our experiments, as the scarcity of extensive annotated dermatology data limits the effectiveness of traditional DL models. The proposed pipeline is validated and tested using two standard datasets, ISIC2016 and PH2. With only 50% annotated samples, the proposed approach demonstrated promising performance with DSC, IoU, and accuracy of 0.96, 0.92, and 0.85 on ISIC2016 and 0.93, 0.88, and 0.93 on cross-data testing with PH 2 dataset. When benchmarked against leading edge models trained on 100% labeled data, MIRA achieved promising results and even outperformed in some cases. These findings show that it can significantly reduce manual annotation requirements while achieving segmentation performance comparable to models trained on fully annotated skin lesion data. © The Author(s) 2025