Browsing by Author "Kadaskar, M."

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ANet: Nuclei Instance Segmentation and Classification with Attention-Based Network
(Springer, 2024) Kadaskar, M.; Patil, N.
The segmentation and classification of nuclei in haematoxylin and eosin-stained images is critical for diagnosing cancer and other disorders. Developing automated methods is necessary for the quantitative analysis of whole-slide images and further downstream analysis. However, many challenges are to be solved, such as varying morphology and observer differences. To address these concerns, we present ANet, an encoder–decoder structure based on attention mechanisms for nuclear segmentation and classification that makes use of information in high-dimensional features improved by attention. These blocks generate meaningful feature activation and eliminate irrelevant information to produce finer maps. It segments the touching, clustered, and overlapping nuclei and classifies them using upsampling branches. Our method includes components such as PreAct-ResNet50, residual attention, convolutional block attention module, and dense attention unit. We demonstrate how our approach achieves cutting-edge performance on several multi-tissue histopathology datasets such as Kumar, CoNSeP, and CPM17. We also demonstrate our model’s generalization capabilities on other combinations of datasets, including CPM15 and TNBC. ANet demonstrates a notable improvement of 1.14%, 2.70%, 1.41%, and 1.29% in Dice, AJI, SQ, and PQ scores, respectively, for the CPM17 dataset. In addition, it achieves a 1.18% improvement in AJI score for the Kumar dataset. Despite the inherent challenges in nuclei classification within the CoNSeP dataset, ANet yields outstanding results, showcasing a substantial improvement of 9.74%, 3.97%, and 0.80% in F1 scores for the inflammatory, spindle, and miscellaneous classes. Furthermore, ANet exhibits strong generalization across the CPM dataset, TNBC, and Combined CoNSeP, with improvements observed in the majority of metrics. The given improvement is justifiable, as are the interpretable visual results. The proposed method is of great potential for analyzing histopathology images, demonstrated by an increment of performance in multiple metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.
Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification
(Springer, 2023) Kadaskar, M.; Patil, N.
Deep learning has recently advanced in its applicability to computer vision challenges, and medical imaging has become the most used technique in histopathology image analysis. Nuclei instance segmentation, detection, and classification are one such task. Reliable analysis of these image slides is critical in cancer identification, treatment, and care. Researchers have recently been interested in this issue. This study reviews the categorization and investigation of strategies utilized in recent works to improve the effectiveness of automated nuclei segmentation, detection, and classification in histopathology images. It critically examines state-of-the-art deep learning techniques, analyzes the trends, identifies the challenges, and highlights and helps with the future directions for research. The taxonomy includes deep learning techniques, enhancement, and optimization methods. The survey findings will help to overcome the challenges of nuclei segmentation, detection, and classification while improving the performance of models and, thus, aid future research plans. Â© 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification
(Springer, 2023) Kadaskar, M.; Patil, N.
Deep learning has recently advanced in its applicability to computer vision challenges, and medical imaging has become the most used technique in histopathology image analysis. Nuclei instance segmentation, detection, and classification are one such task. Reliable analysis of these image slides is critical in cancer identification, treatment, and care. Researchers have recently been interested in this issue. This study reviews the categorization and investigation of strategies utilized in recent works to improve the effectiveness of automated nuclei segmentation, detection, and classification in histopathology images. It critically examines state-of-the-art deep learning techniques, analyzes the trends, identifies the challenges, and highlights and helps with the future directions for research. The taxonomy includes deep learning techniques, enhancement, and optimization methods. The survey findings will help to overcome the challenges of nuclei segmentation, detection, and classification while improving the performance of models and, thus, aid future research plans. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Nuclei Classification in Histopathology Images Using Fuzzy Ensemble of Convolutional Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2023) Kadaskar, M.; Patil, N.
The development of computational pathology and healthcare studies depends on cell nuclei classification. It has several lifesaving uses, particularly in cancer diagnosis. It is helpful in various applications, including disease diagnosis and medicinal therapy. Due to the number of problems, human examination of these image slides is unpleasant and time-consuming. Morphological traits add to the intricacy. We suggested a fuzzy distance ensemble of Convolutional Neural Networks to achieve state-of-the-art nucleus classification performance. This new model is easier to train and makes more accurate predictions using underlying basic classifiers. To examine how well our model worked, we used the PanNuke dataset. Our model's scores are 0.811 for accuracy, 0.811 for F1, 0.815 for precision, and 0.809 for recall. Â© 2023 IEEE.
Nuclei Segmentation using EfficientNetV2 and Convolutional Block Attention Module
(Institute of Electrical and Electronics Engineers Inc., 2023) Kadaskar, M.; Patil, N.
Cell nuclei segmentation is crucial for developing digital pathology and medical research. It is helpful in numerous applications, including illness diagnostics and other medical therapies. Unfortunately, due to the massive number of nuclei, manual analysis of these image slides takes time and effort. Morphometric appearances add to the complexity. Nevertheless, we upgraded the Nested UNet with an EfficientNetV2S backbone and added a CBAM module in the decoder layers to obtain state-of-the-art performance in medical imaging. This improved model is easier to train, and attention blocks help to tune the retrieved features for better performance. We tested our model using the CryoNuSeg dataset to see how well it performed. Our model scores 0.941 on Dice, 0.605 on AJI, and 0.614 on PQ. Â© 2023 IEEE.