Faculty Publications

Now showing 1 - 3 of 3

Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images
(Elsevier Ltd, 2021) Aatresh, A.A.; Yatgiri, R.P.; Chanchal, A.K.; Kumar, A.; Ravi, A.; Das, D.; Raghavendra, B.S.; Lal, S.; Kini, J.
Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet. © 2021 Elsevier Ltd
High-resolution deep transferred ASPPU-Net for nuclei segmentation of histopathology images
(Springer Science and Business Media Deutschland GmbH, 2021) Chanchal, A.K.; Lal, S.; Kini, J.
Purpose: Increasing cancer disease incidence worldwide has become a major public health issue. Manual histopathological analysis is a common diagnostic method for cancer detection. Due to the complex structure and wide variability in the texture of histopathology images, it has been challenging for pathologists to diagnose manually those images. Automatic segmentation of histopathology images to diagnose cancer disease is a continuous exploration field in recent times. Segmentation and analysis for diagnosis of histopathology images by using an efficient deep learning algorithm are the purpose of the proposed method. Method: To improve the segmentation performance, we proposed a deep learning framework that consists of a high-resolution encoder path, an atrous spatial pyramid pooling bottleneck module, and a powerful decoder. Compared to the benchmark segmentation models having a deep and thin path, our network is wide and deep that effectively leverages the strength of residual learning as well as encoder–decoder architecture. Results: We performed careful experimentation and analysis on three publically available datasets namely kidney dataset, Triple Negative Breast Cancer (TNBC) dataset, and MoNuSeg histopathology image dataset. We have used the two most preferred performance metrics called F1 score and aggregated Jaccard index (AJI) to evaluate the performance of the proposed model. The measured values of F1 score and AJI score are (0.9684, 0.9394), (0.8419, 0.7282), and (0.8344, 0.7169) on the kidney dataset, TNBC histopathology dataset, and MoNuSeg dataset, respectively. Conclusion
Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images
(Nature Research, 2025) Chanchal, A.K.; Lal, S.; Suresh, S.
Kidney cancer is a leading cause of cancer-related mortality, with renal cell carcinoma (RCC) being the most prevalent form, accounting for 80–85% of all renal tumors. Traditional diagnosis of kidney cancer requires manual examination and analysis of histopathology images, which is time-consuming, error-prone, and depends on the pathologist’s expertise. Recently, deep learning algorithms have gained significant attention in histopathology image analysis. In this study, we developed an efficient and robust deep learning architecture called RenalNet for the classification of subtypes of RCC from kidney histopathology images. The RenalNet is designed to capture cross-channel and inter-spatial features at three different scales simultaneously and combine them together. Cross-channel features refer to the relationships and dependencies between different data channels, while inter-spatial features refer to patterns within small spatial regions. The architecture contains a CNN module called multiple channel residual transformation (MCRT), to focus on the most relevant morphological features of RCC by fusing the information from multiple paths. Further, to improve the network’s representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. As a part of this study, we also introduced a novel benchmark dataset for the classification of subtypes of RCC from kidney histopathology images. We obtained digital hematoxylin and eosin (H&E) stained WSIs from The Cancer Genome Atlas (TCGA) and acquired region of interest (ROIs) under the supervision of experienced pathologists resulted in the creation of patches. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on three well-known datasets. Compared to the best-performing state-of-the-art model, RenalNet achieves accuracies of 91.67%, 97.14%, and 97.24% on three different datasets. Additionally, the proposed method significantly reduces the number of parameters and FLOPs, demonstrating computationally efficient with 2.71 × FLOPs & 0.2131 × parameters. © The Author(s) 2025.

Faculty Publications

Browse

Filters

Settings

Sort By

Results per page

Search Results