arXiv subjects

Arnav Bhavsar

Publications and source records attributed to Arnav Bhavsar.

32 records · Page 2Linked to original sources

arXiv2022

Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection

Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may be important for high throughput screening, is also not well explored.To address this challenge, we consider some contemporary CNN models for binary classification of breast histopathology images that involves (1) the data preprocessing with stain normalized images using an adaptive colour deconvolution (ACD) based color normalization algorithm to handle the stain variabilities; and (2) applying transfer learning based training of some arguably more efficient CNN models, namely Visual Geometry Group Network (VGG16), MobileNet and EfficientNet. We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images. The experimental analysis shows that pretrained networks in most cases yield better quality results on data augmented breast histopathology images with stain normalization, than the case without stain normalization. Further, we evaluated the performance and efficiency of popular lightweight networks using stain normalized images and found that EfficientNet outperforms VGG16 and MobileNet in terms of test accuracy and F1 Score. We observed that efficiency in terms of test time is better in EfficientNet than other networks; VGG Net, MobileNet, without much drop in the classification accuracy.

eess.IV↗

arXiv2021

MHATC: Autism Spectrum Disorder identification utilizing multi-head attention encoder along with temporal consolidation modules

Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disorder (ASD) by using network-based functional connectivity. It has been shown that ASD is associated with brain regions and their inter-connections. However, discriminating based on connectivity patterns among imaging data of the control population and that of ASD patients' brains is a non-trivial task. In order to tackle said classification task, we propose a novel deep learning architecture (MHATC) consisting of multi-head attention and temporal consolidation modules for classifying an individual as a patient of ASD. The devised architecture results from an in-depth analysis of the limitations of current deep neural network solutions for similar applications. Our approach is not only robust but computationally efficient, which can allow its adoption in a variety of other research and clinical settings.

q-bio.NC↗

arXiv2021

Virtual Screening of Pharmaceutical Compounds with hERG Inhibitory Activity (Cardiotoxicity) using Ensemble Learning

In silico prediction of cardiotoxicity with high sensitivity and specificity for potential drug molecules can be of immense value. Hence, building machine learning classification models, based on some features extracted from the molecular structure of drugs, which are capable of efficiently predicting cardiotoxicity is critical. In this paper, we consider the application of various machine learning approaches, and then propose an ensemble classifier for the prediction of molecular activity on a Drug Discovery Hackathon (DDH) (1st reference) dataset. We have used only 2-D descriptors of SMILE notations for our prediction. Our ensemble classification uses 5 classifiers (2 Random Forest Classifiers, 2 Support Vector Machines and a Dense Neural Network) and uses Max-Voting technique and Weighted-Average technique for final decision.

q-bio.QM↗

arXiv2020

Semantic Features Aided Multi-Scale Reconstruction of Inter-Modality Magnetic Resonance Images

Long acquisition time (AQT) due to series acquisition of multi-modality MR images (especially T2 weighted images (T2WI) with longer AQT), though beneficial for disease diagnosis, is practically undesirable. We propose a novel deep network based solution to reconstruct T2W images from T1W images (T1WI) using an encoder-decoder architecture. The proposed learning is aided with semantic features by using multi-channel input with intensity values and gradient of image in two orthogonal directions. A reconstruction module (RM) augmenting the network along with a domain adaptation module (DAM) which is an encoder-decoder model built-in with sharp bottleneck module (SBM) is trained via modular training. The proposed network significantly reduces the total AQT with negligible qualitative artifacts and quantitative loss (reconstructs one volume in approximately 1 second). The testing is done on publicly available dataset with real MR images, and the proposed network shows (approximately 1dB) increase in PSNR over SOTA.

eess.IV↗

arXiv2020

Detecting Deepfakes with Metric Learning

With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenario and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.

cs.CV↗

arXiv2019

Copy-Move Forgery Classification via Unsupervised Domain Adaptation

In the current era, image manipulation is becoming increasingly easier, yielding more natural looking images, owing to the modern tools in image processing and computer vision techniques. The task of the segregation of forged images has become very challenging. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting algorithm. Furthermore, we use an unsupervised domain adaptation network to detect copy-move forgery in images. Our approach can be helpful in those cases, where the classification of data is unavailable.

cs.CV↗

arXiv2018

Role of Class-specific Features in Various Classification Frameworks for Human Epithelial (HEp-2) Cell Images

The antinuclear antibody detection with human epithelial cells is a popular approach for autoimmune diseases diagnosis. The manual evaluation demands time, effort and capital, and automation in screening can greatly aid the physicians in these respects. In this work, we employ simple, efficient and visually more interpretable, class-specific features which defined based on the visual characteristics of each class. We believe that defining features with a good visual interpretation, is indeed important in a scenario, where such an approach is used in an interactive CAD system for pathologists. Considering that problem consists of few classes, and our rather simplistic feature definitions, frameworks can be structured as hierarchies of various binary classifiers. These variants include frameworks which are earlier explored and some which are not explored for this task. We perform various experiments which include traditional texture features and demonstrate the effectiveness of class-specific features in various frameworks. We make insightful comparisons between different types of classification frameworks given their silent aspects and pros and cons over each other. We also demonstrate an experiment with only intermediates samples for testing. The proposed work yields encouraging results with respect to the state-of-the-art and highlights the role of class-specific features in different classification frameworks.

cs.CV↗

arXiv2018

Considerations for a PAP Smear Image Analysis System with CNN Features

It has been shown that for automated PAP-smear image classification, nucleus features can be very informative. Therefore, the primary step for automated screening can be cell-nuclei detection followed by segmentation of nuclei in the resulting single cell PAP-smear images. We propose a patch based approach using CNN for segmentation of nuclei in single cell images. We then pose the question of ion of segmentation for classification using representation learning with CNN, and whether low-level CNN features may be useful for classification. We suggest a CNN-based feature level analysis and a transfer learning based approach for classification using both segmented as well full single cell images. We also propose a decision-tree based approach for classification. Experimental results demonstrate the effectiveness of the proposed algorithms individually (with low-level CNN features), and simultaneously proving the sufficiency of cell-nuclei detection (rather than accurate segmentation) for classification. Thus, we propose a system for analysis of multi-cell PAP-smear images consisting of a simple nuclei detection algorithm followed by classification using transfer learning.

cs.CV↗

arXiv2018

Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints

In this work, we address a challenging problem of fine-grained and coarse-grained recognition of object manipulation actions. Due to the variations in geometrical and motion constraints, there are different manipulations actions possible to perform different sets of actions with an object. Also, there are subtle movements involved to complete most of object manipulation actions. This makes the task of object manipulation action recognition difficult with only just the motion information. We propose to use grasp and motion-constraints information to recognise and understand action intention with different objects. We also provide an extensive experimental evaluation on the recent Yale Human Grasping dataset consisting of large set of 455 manipulation actions. The evaluation involves a) Different contemporary multi-class classifiers, and binary classifiers with one-vs-one multi- class voting scheme, b) Differential comparisons results based on subsets of attributes involving information of grasp and motion-constraints, c) Fine-grained and Coarse-grained object manipulation action recognition based on fine-grained as well as coarse-grained grasp type information, and d) Comparison between Instance level and Sequence level modeling of object manipulation actions. Our results justifies the efficacy of grasp attributes for the task of fine-grained and coarse-grained object manipulation action recognition.

cs.CV↗

arXiv2018

Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images

Increasing demand for high field magnetic resonance (MR) scanner indicates the need for high-quality MR images for accurate medical diagnosis. However, cost constraints, instead, motivate a need for algorithms to enhance images from low field scanners. We propose an approach to process the given low field (3T) MR image slices to reconstruct the corresponding high field (7T-like) slices. Our framework involves a novel architecture of a merged convolutional autoencoder with a single encoder and multiple decoders. Specifically, we employ three decoders with random initializations, and the proposed training approach involves selection of a particular decoder in each weight-update iteration for back propagation. We demonstrate that the proposed algorithm outperforms some related contemporary methods in terms of performance and reconstruction time.

cs.CV↗

arXiv2017

Siamese LSTM based Fiber Structural Similarity Network (FS2Net) for Rotation Invariant Brain Tractography Segmentation

In this paper, we propose a novel deep learning architecture combining stacked Bi-directional LSTM and LSTMs with the Siamese network architecture for segmentation of brain fibers, obtained from tractography data, into anatomically meaningful clusters. The proposed network learns the structural difference between fibers of different classes, which enables it to classify fibers with high accuracy. Importantly, capturing such deep inter and intra class structural relationship also ensures that the segmentation is robust to relative rotation among test and training data, hence can be used with unregistered data. Our extensive experimentation over order of hundred-thousands of fibers show that the proposed model achieves state-of-the-art results, even in cases of large relative rotations between test and training data.

cs.CV↗

arXiv2016

Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation

In recent years, the usefulness of 3D shape estimation is being realized in microscopic or close-range imaging, as the 3D information can further be used in various applications. Due to limited depth of field at such small distances, the defocus blur induced in images can provide information about the 3D shape of the object. The task of `shape from defocus' (SFD), involves the problem of estimating good quality 3D shape estimates from images with depth-dependent defocus blur. While the research area of SFD is quite well-established, the approaches have largely demonstrated results on objects with bulk/coarse shape variation. However, in many cases, objects studied under microscopes often involve fine/detailed structures, which have not been explicitly considered in most methods. In addition, given that, in recent years, large data volumes are typically associated with microscopy related applications, it is also important for such SFD methods to be efficient. In this work, we provide an indication of the usefulness of the Belief Propagation (BP) approach in addressing these concerns for SFD. BP has been known to be an efficient combinatorial optimization approach, and has been empirically demonstrated to yield good quality solutions in low-level vision problems such as image restoration, stereo disparity estimation etc. For exploiting the efficiency of BP in SFD, we assume local space-invariance of the defocus blur, which enables the application of BP in a straightforward manner. Even with such an assumption, the ability of BP to provide good quality solutions while using non-convex priors, reflects in yielding plausible shape estimates in presence of fine structures on the objects under microscopy imaging.

cs.CV↗

arXiv2012

Analysis of Magnification in Depth from Defocus

In depth from defocus (DFD), when images are captured with different camera parameters, a relative magnification is induced between them. Image warping is a simpler solution to account for magnification than seemingly more accurate optical approaches. This work is an investigation into the effects of magnification on the accuracy of DFD. We comment on issues regarding scaling effect on relative blur computation. We statistically analyze accountability of scale factor, commenting on the bias and efficiency of the estimator that does not consider scale. We also discuss the effect of interpolation errors on blur estimation in a warping based solution to handle magnification and carry out experimental analysis to comment on the blur estimation accuracy.

cs.CV↗

arXiv2012

Resolution Enhancement of Range Images via Color-Image Segmentation

We report a method for super-resolution of range images. Our approach leverages the interpretation of LR image as sparse samples on the HR grid. Based on this interpretation, we demonstrate that our recently reported approach, which reconstructs dense range images from sparse range data by exploiting a registered colour image, can be applied for the task of resolution enhancement of range images. Our method only uses a single colour image in addition to the range observation in the super-resolution process. Using the proposed approach, we demonstrate super-resolution results for large factors (e.g. 4) with good localization accuracy.

cs.CV↗