activity
20192026
most citedAnalyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

8 citations · 12 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2026

Combining Residual U-Net and Data Augmentation for Dense Temporal Segmentation of Spike Wave Discharges in Single-Channel EEG

Saurav Sengupta, Scott Kilianski, Suchetha Sharma +4

Manual annotation of spike-wave discharges (SWDs), the electrographic hallmark of absence seizures, is labor-intensive for long-term electroencephalography (EEG) monitoring studies…

cs.CV2025

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions

Saurav Sengupta, Nazanin Moradinasab, Jiebei Liu +1

Recent research suggests that Vision Language Models (VLMs) often rely on inherent biases learned during training when responding to queries about visual properties of images. Thes…

cs.CV2025

Examining Vision Language Models through Multi-dimensional Experiments with Vision and Text Features

Saurav Sengupta, Nazanin Moradinasab, Jiebei Liu +1

Recent research on Vision Language Models (VLMs) suggests that they rely on inherent biases learned during training to respond to questions about visual properties of an image. The…

cs.LG2025

Towards Robust Multimodal Representation: A Unified Approach with Adaptive Experts and Alignment

Nazanin Moradinasab, Saurav Sengupta, Jiebei Liu +2

Healthcare relies on multiple types of data, such as medical images, genetic information, and clinical records, to improve diagnosis and treatment. However, missing data is a commo…

cs.CV2023★ 4 cited

Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERT

Saurav Sengupta, Donald E. Brown

Deep learning for histopathology has been successfully used for disease classification, image segmentation and more. However, combining image and text modalities using current stat…

cs.CV2023

Automatic Report Generation for Histopathology images using pre-trained Vision Transformers

Saurav Sengupta, Donald E. Brown

Deep learning for histopathology has been successfully used for disease classification, image segmentation and more. However, combining image and text modalities using current stat…