2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Deep neural networks suffer from Catastrophic Forgetting (CF) on old tasks when they are trained ... more Deep neural networks suffer from Catastrophic Forgetting (CF) on old tasks when they are trained to learn new tasks sequentially, since the parameters of the model will change to optimize on the new class. The problem of alleviating CF is of interest to Computer aided diagnostic (CAD) systems community to facilitate class incremental learning (IL): learn new classes as and when new data/annotations are made available and old data is no longer accessible. However, IL has not been explored much in CAD development. We propose a novel approach that ensures that a model remembers the causal factor behind the decisions on the old classes, while incrementally learning new classes. We introduce a common auxiliary task during the course of incremental training, whose hidden representations are shared across all the classification heads. Since the hidden representation is no longer task-specific, it leads to a significant reduction in CF. We demonstrate our approach by incrementally learning 5 different tasks on Chest-Xrays and compare the results with the state-of-the-art regularization methods. Our approach performs consistently well in reducing CF in all the tasks with almost zero CF in most of the cases unlike standard regularisation-based approaches.
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Segmentation and analysis of sub-cortical structures is of interest in diagnosing some neurologic... more Segmentation and analysis of sub-cortical structures is of interest in diagnosing some neurological diseases. Segmentation is a challenging task because of brain tissue ambiguity and data scarcity. Deep learning (DL) solutions are widely used for this purpose by considering the problem as a semantic segmentation of the brain. In general, DL approaches exhibit a bias towards larger structures when training is done on the whole brain. We propose a method to address this problem wherein a pre-training step is used to learn tissue characteristics and a rough ROI extraction step aids focusing on local context. We use a Residual U-net for demonstrating the proposed method. Experiments on the IBSR and MICCAI datasets show that our proposed solution leads to an improvement in segmentation performance in general with medium and small size structures benefiting significantly. The performance with the proposed method is also marginally better than a more complex, state of art sub-cortical structure segmentation method. A strength of the proposed method is that it can also be applied as a modification to any existing segmentation solution.
Deep learning based methods have shown great promise in achieving accurate automatic detection of... more Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (covid) - 19 from Chest X-Ray (cxr) images.However, incorporating explainability in these solutions remains relatively less explored. We present a hierarchical classification approach for separating normal, non-covid pneumonia (ncp) and covid cases using cxr images. We demonstrate that the proposed method achieves clinically consistent explainations. We achieve this using a novel multi-scale attention architecture called Multi-scale Attention Residual Learning (marl) and a new loss function based on conicity for training the proposed architecture. The proposed classification strategy has two stages. The first stage uses a model derived from DenseNet to separate pneumonia cases from normal cases while the second stage uses the marl architecture to discriminate between covid and ncp cases. With a five-fold cross validation the proposed method achieves 93%, 96.28%, and 84.51% accuracy respectively over three large, public datasets for normal vs. ncp vs. covid classification. This is competitive to the state-of-the-art methods. We also provide explanations in the form of GradCAM attributions, which are well aligned with expert annotations. The attributions are also seen to clearly indicate that marl deems the peripheral regions of the lungs to be more important in the case of covid cases while central regions are seen as more important in ncp cases. This observation matches the criteria described by radiologists in clinical literature, thereby attesting to the utility of the derived explanations.
2020 IEEE International Conference on Image Processing (ICIP), 2020
Disease diagnosis often requires segmentation of structures from a given image followed by shape ... more Disease diagnosis often requires segmentation of structures from a given image followed by shape analysis. Shape analysis entails quantifying the variability in a shape by constructing a template for a given population. We propose an orientation-invariant representation using varifolds for the shape elements in a given shape population and present a novel diffeomorphic Log-demons based template creation pipeline. The proposed method generates a good quality template at a significantly less computation time compared to state of the art method.
Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Alt... more Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The assessment involves segmentation of optic disk and cup region within the ONH. In this paper, we present a comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts. The dataset also provides expert opinion on an image representing a normal or glaucomatous eye and on the presence of notching in an image. Several state of the art methods are assessed against this dataset using cup to disc diameter ratio (CDR), area and boundary-based evaluation measures. These are presented to aid benchmarking of new methods. A supervised, notch detection method based on the segmentation results is also proposed and its assessment results are i...
Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by ... more Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by the underlying deformation, captured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. SVF based methods form a metric-free framework which captures a finite dimensional submanifold of deformations embedded in the infinite dimensional smooth manifold of diffeomorphisms. However, these methods cover only a limited degree of deformations. In this paper, we address this limitation and define an approximate metric space for the manifold of diffeomorphisms $\mathcal{G}$. We propose a method to break down the large deformation into finite compositions of small deformations. This results in a broken geodesic path on $\mathcal{G}$ and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the...
Context: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroima... more Context: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroimage analysis tasks as it provides an atlas with a standard coordinate system which is needed for spatial normalization of a brain MRI. Ideally, this atlas should be as near to the average brain of the population being studied as possible. Aims: The aim of this study is to construct and validate the Indian brain MRI atlas of young Indian population and the corresponding structure probability maps. Settings and Design: This was a population-specific atlas generation and validation process. Materials and Methods: 100 young healthy adults (M/F = 50/50), aged 21–30 years, were recruited for the study. Three different 1.5-T scanners were used for image acquisition. The atlas and structure maps were created using nonrigid groupwise registration and label-transfer techniques. Comparison and Validation: The generated atlas was compared against other atlases to study the population-specific trends....
Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective di... more Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective diagnosis of melanoma is of interest. Current approaches for automated diagnosis of melanoma either use pattern recognition or analytical recognition like ABCDE (asymmetry, border, color, diameter and evolving) criterion. In practice however, a differential approach wherein outliers (ugly duckling) are detected and used to evaluate nevi/lesions. Incorporation of differential recognition in Computer Aided Diagnosis (CAD) systems has not been explored but can be beneficial as it can provide a clinical justification for the derived decision. We present a method for identifying and quantifying ugly ducklings by performing Intra-Patient Comparative Analysis (IPCA) of neighboring nevi. This is then incorporated in a CAD system design for melanoma detection. This design ensures flexibility to handle cases where IPCA is not possible. Our experiments on a public dataset show that the outlier inform...
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Deep neural networks suffer from Catastrophic Forgetting (CF) on old tasks when they are trained ... more Deep neural networks suffer from Catastrophic Forgetting (CF) on old tasks when they are trained to learn new tasks sequentially, since the parameters of the model will change to optimize on the new class. The problem of alleviating CF is of interest to Computer aided diagnostic (CAD) systems community to facilitate class incremental learning (IL): learn new classes as and when new data/annotations are made available and old data is no longer accessible. However, IL has not been explored much in CAD development. We propose a novel approach that ensures that a model remembers the causal factor behind the decisions on the old classes, while incrementally learning new classes. We introduce a common auxiliary task during the course of incremental training, whose hidden representations are shared across all the classification heads. Since the hidden representation is no longer task-specific, it leads to a significant reduction in CF. We demonstrate our approach by incrementally learning 5 different tasks on Chest-Xrays and compare the results with the state-of-the-art regularization methods. Our approach performs consistently well in reducing CF in all the tasks with almost zero CF in most of the cases unlike standard regularisation-based approaches.
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
Segmentation and analysis of sub-cortical structures is of interest in diagnosing some neurologic... more Segmentation and analysis of sub-cortical structures is of interest in diagnosing some neurological diseases. Segmentation is a challenging task because of brain tissue ambiguity and data scarcity. Deep learning (DL) solutions are widely used for this purpose by considering the problem as a semantic segmentation of the brain. In general, DL approaches exhibit a bias towards larger structures when training is done on the whole brain. We propose a method to address this problem wherein a pre-training step is used to learn tissue characteristics and a rough ROI extraction step aids focusing on local context. We use a Residual U-net for demonstrating the proposed method. Experiments on the IBSR and MICCAI datasets show that our proposed solution leads to an improvement in segmentation performance in general with medium and small size structures benefiting significantly. The performance with the proposed method is also marginally better than a more complex, state of art sub-cortical structure segmentation method. A strength of the proposed method is that it can also be applied as a modification to any existing segmentation solution.
Deep learning based methods have shown great promise in achieving accurate automatic detection of... more Deep learning based methods have shown great promise in achieving accurate automatic detection of Coronavirus Disease (covid) - 19 from Chest X-Ray (cxr) images.However, incorporating explainability in these solutions remains relatively less explored. We present a hierarchical classification approach for separating normal, non-covid pneumonia (ncp) and covid cases using cxr images. We demonstrate that the proposed method achieves clinically consistent explainations. We achieve this using a novel multi-scale attention architecture called Multi-scale Attention Residual Learning (marl) and a new loss function based on conicity for training the proposed architecture. The proposed classification strategy has two stages. The first stage uses a model derived from DenseNet to separate pneumonia cases from normal cases while the second stage uses the marl architecture to discriminate between covid and ncp cases. With a five-fold cross validation the proposed method achieves 93%, 96.28%, and 84.51% accuracy respectively over three large, public datasets for normal vs. ncp vs. covid classification. This is competitive to the state-of-the-art methods. We also provide explanations in the form of GradCAM attributions, which are well aligned with expert annotations. The attributions are also seen to clearly indicate that marl deems the peripheral regions of the lungs to be more important in the case of covid cases while central regions are seen as more important in ncp cases. This observation matches the criteria described by radiologists in clinical literature, thereby attesting to the utility of the derived explanations.
2020 IEEE International Conference on Image Processing (ICIP), 2020
Disease diagnosis often requires segmentation of structures from a given image followed by shape ... more Disease diagnosis often requires segmentation of structures from a given image followed by shape analysis. Shape analysis entails quantifying the variability in a shape by constructing a template for a given population. We propose an orientation-invariant representation using varifolds for the shape elements in a given shape population and present a novel diffeomorphic Log-demons based template creation pipeline. The proposed method generates a good quality template at a significantly less computation time compared to state of the art method.
Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Alt... more Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The assessment involves segmentation of optic disk and cup region within the ONH. In this paper, we present a comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts. The dataset also provides expert opinion on an image representing a normal or glaucomatous eye and on the presence of notching in an image. Several state of the art methods are assessed against this dataset using cup to disc diameter ratio (CDR), area and boundary-based evaluation measures. These are presented to aid benchmarking of new methods. A supervised, notch detection method based on the segmentation results is also proposed and its assessment results are i...
Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by ... more Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by the underlying deformation, captured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. SVF based methods form a metric-free framework which captures a finite dimensional submanifold of deformations embedded in the infinite dimensional smooth manifold of diffeomorphisms. However, these methods cover only a limited degree of deformations. In this paper, we address this limitation and define an approximate metric space for the manifold of diffeomorphisms $\mathcal{G}$. We propose a method to break down the large deformation into finite compositions of small deformations. This results in a broken geodesic path on $\mathcal{G}$ and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the...
Context: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroima... more Context: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroimage analysis tasks as it provides an atlas with a standard coordinate system which is needed for spatial normalization of a brain MRI. Ideally, this atlas should be as near to the average brain of the population being studied as possible. Aims: The aim of this study is to construct and validate the Indian brain MRI atlas of young Indian population and the corresponding structure probability maps. Settings and Design: This was a population-specific atlas generation and validation process. Materials and Methods: 100 young healthy adults (M/F = 50/50), aged 21–30 years, were recruited for the study. Three different 1.5-T scanners were used for image acquisition. The atlas and structure maps were created using nonrigid groupwise registration and label-transfer techniques. Comparison and Validation: The generated atlas was compared against other atlases to study the population-specific trends....
Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective di... more Melanoma is a leading cause of deaths due to skin cancer deaths and hence, early and effective diagnosis of melanoma is of interest. Current approaches for automated diagnosis of melanoma either use pattern recognition or analytical recognition like ABCDE (asymmetry, border, color, diameter and evolving) criterion. In practice however, a differential approach wherein outliers (ugly duckling) are detected and used to evaluate nevi/lesions. Incorporation of differential recognition in Computer Aided Diagnosis (CAD) systems has not been explored but can be beneficial as it can provide a clinical justification for the derived decision. We present a method for identifying and quantifying ugly ducklings by performing Intra-Patient Comparative Analysis (IPCA) of neighboring nevi. This is then incorporated in a CAD system design for melanoma detection. This design ensures flexibility to handle cases where IPCA is not possible. Our experiments on a public dataset show that the outlier inform...
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