arXiv subjects

Vamshi Krishna Kancharla

Publications and source records attributed to Vamshi Krishna Kancharla.

6 recordsLinked to original sources

arXiv2026

Sex-based Network-Specific Differences in Connectomes: A Krakencoder-Based Analysis

This study examines how deficiencies in one brain connectome modality propagate to the other, using the Krakencoder as a simulation framework. Structural and functional connectomes from 702 healthy participants in the Human Connectome Project were analyzed, with the impact of each of the Yeo-7 functional networks assessed separately. Seven scenarios were considered, each involving the removal of a single network while the remaining networks were preserved. The resulting perturbations in cross-modal predictions were quantified using three complementary metrics: KL divergence on eigenvalue spectra, Frobenius norm, and Wasserstein distance. In addition, the persistence of sex-specific information within the predicted connectomes was evaluated. Across all metrics and both prediction directions, the Default Mode Network produced the largest perturbations, whereas the Somatomotor network yielded the smallest. Sex differences in network-level perturbation signatures were subtle, with the best result being an accuracy of 66.09% from connectomes predicted under network-removal conditions. In contrast, connectomes predicted from intact inputs achieved substantially higher sex classification accuracy, reaching up to 84.76%. These findings confirm that full predicted connectomes retain considerably more sex-discriminative information than perturbation-derived signatures alone.

cs.CV↗

arXiv2026

Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised $β$-VAE and Quantum Kernels

This paper presents a two-stage Hybrid Classical-Quantum (HCQ) pipeline for binary Alzheimer's disease (AD) classification from 3D T1-weighted structural MRI volumes, where the classical and quantum components are designed to complement each other rather than operate independently. A supervised 3D $β$-variational autoencoder (VAE) is trained end-to-end under voxel-wise reconstruction, KL-divergence, and focal classification losses that compress each 3D MRI volume (resized from 152 x 184 x 152 to 96 x 96 x 96) into a 64-dimensional latent code. Partial Least Squares (PLS) regression selects the six components in the latent code that best separate Alzheimer's Disease (AD) from cognitively normal (CN) subjects and rescales them into rotation angles, which are encoded onto a six-qubit register using the ZZ quantum feature map to give us the respective quantum states. The input to a precomputed-kernel Support Vector Machine (SVM) is an N x N Gram matrix (N = 308), created by calculating the overlap between every pair of quantum states. The novelty of this work lies in the fact that the quantum kernel operates directly on disease-aware features that are learned end-to-end by a supervised autoencoder, rather than on pre-extracted inputs. On 308 ADNI-1 subjects, consisting of 137 AD and 171 CN subjects, the baseline achieved 67.2% accuracy and 0.759 AUC, while the stability-enhanced variant reached 72.1% accuracy and 0.799 AUC with cross-fold variance halved. 3D Grad-CAM further helped validate our model's focus on brain regions linked to Alzheimer's. The HCQ pipeline could serve as a general-purpose framework for diagnostic classification across biomedical imaging domains that present similar challenges for classical approaches.

cs.CV↗

arXiv2026

Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet-B7 for Improved Gastrointestinal Abnormality Classification in Video Capsule Endoscopy

Video Capsule Endoscopy (VCE) has become an indispensable diagnostic tool for gastrointestinal (GI) disorders due to its non-invasive nature and ability to capture high-resolution images of the small intestine. However, the enormous volume of data generated during a single procedure makes manual inspection labor-intensive, time-consuming, and prone to inter-observer variability. Automated analysis using deep learning offers a promising solution, but its effectiveness is often limited by data imbalance and the high cost of labeled medical data. In this work, we propose a novel framework that combines self-supervised learning through a U-Net-based masked autoencoder with supervised feature extraction using EfficientNet-B7 for multi-class abnormality classification in VCE images. The U-Net model is first trained in a self-supervised manner using Gaussian noise removal and masked reconstruction to learn robust visual representations without requiring annotations. The learned encoder features are then fused with EfficientNet-B7 features to form a rich, discriminative representation for classification. We evaluate our approach on the Capsule Vision 2024 Challenge dataset consisting of ten abnormality classes and a dominant normal class. Experimental results demonstrate that the proposed fusion framework achieves a validation accuracy of 94\%, outperforming standalone architectures and attention-based fusion variants. The study highlights the effectiveness of self-supervised representation learning and feature fusion in addressing class imbalance and improving diagnostic accuracy in real-world medical imaging scenarios.

cs.CV↗

arXiv2024

MRI Volume-Based Robust Brain Age Estimation Using Weight-Shared Spatial Attention in 3D CNNs

Important applications of advancements in machine learning, are in the area of healthcare, more so for neurological disorder detection. A crucial step towards understanding the neurological status, is to estimate the brain age using structural MRI volumes, in order to measure its deviation from chronological age. Factors that contribute to brain age are best captured using a data-driven approach, such as deep learning. However, it places a huge demand on the availability of diverse datasets. In this work, we propose a robust brain age estimation paradigm that utilizes a 3D CNN model, by-passing the need for model-retraining across datasets. The proposed model consists of seven 3D CNN layers, with a shared spatial attention layer incorporated at each CNN layer followed by five dense layers. The novelty of the proposed method lies in the idea of spatial attention module, with shared weights across the CNN layers. This weight sharing ensures directed attention to specific brain regions, for localizing age-related features within the data, lending robustness. The proposed model, trained on ADNI dataset comprising 516 T1 weighted MRI volumes of healthy subjects, resulted in Mean Absolute Error (MAE) of 1.662 years, which is an improvement of 1.688 years over the state-of-the-art (SOTA) model, based on disjoint test samples from the same repository. To illustrate generalizability, the same pipeline was utilized on volumes from a publicly available source called OASIS3. From OASIS3, MRI volumes 890 healthy subjects were utilized resulting in MAE of 2.265 years. Due to diversity in acquisitions across multiple sites, races and genetic factors, traditional CNN models are not guaranteed to prioritize brain regions crucial for age estimation. In contrast, the proposed weight-shared spatial attention module, directs attention on specific regions, required for the estimation.

eess.IV↗

arXiv2024

Overcoming Scene Context Constraints for Object Detection in wild using Defilters

This paper focuses on improving object detection performance by addressing the issue of image distortions, commonly encountered in uncontrolled acquisition environments. High-level computer vision tasks such as object detection, recognition, and segmentation are particularly sensitive to image distortion. To address this issue, we propose a novel approach employing an image defilter to rectify image distortion prior to object detection. This method enhances object detection accuracy, as models perform optimally when trained on non-distorted images. Our experiments demonstrate that utilizing defiltered images significantly improves mean average precision compared to training object detection models on distorted images. Consequently, our proposed method offers considerable benefits for real-world applications plagued by image distortion. To our knowledge, the contribution lies in employing distortion-removal paradigm for object detection on images captured in natural settings. We achieved an improvement of 0.562 and 0.564 of mean Average precision on validation and test data.

cs.CV↗

arXiv2023

Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5

Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades. The corpus callosum (CC) is the principal white matter fibre tract, enabling all kinds of inter-hemispheric communication. Thus, subtle changes in CC might be associated with various neurological disorders. The present work proposes the potential of YOLOv5-based CC detection framework to differentiate atypical Parkinsonian disorders (PD) from healthy controls (HC). With 3 rounds of hold-out validation, mean classification accuracy of 92% is obtained using the proposed method on a proprietary dataset consisting of 20 healthy subjects and 20 cases of APDs, with an improvement of 5% over SOTA methods (CC morphometry and visual texture analysis) that used the same dataset. Subsequently, in order to incorporate the explainability of YOLO predictions, Eigen CAM based heatmap is generated for identifying the most important sub-region in CC that leads to the classification. The result of Eigen CAM showed CC mid-body as the most distinguishable sub-region in classifying APDs and HC, which is in-line with SOTA methodologies and the current prevalent understanding in medicine.

eess.IV↗