1 citations · 1 across the 8 of their papers we have counts for
10 papers
EvoCowork: A Benchmark for Real-World Office Tasks
Zhou Liu, Xingyan Chen, ZhaoYang Han +6
Office workflows are a demanding testbed for self-improving agents because their deliverables must be correct, natively editable, and mutually consistent. A central question is whe…
Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction
Saarang Panchavati, Uddhav Panchavati, Hiroki Nariai +2
Electroencephalography (EEG) is a widely used tool for studying brain function, with applications in clinical neuroscience, diagnosis, and brain-computer interfaces (BCIs). Recent…
US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound
Ashwath Radhachandran, Vedrana Ivezić, Shreeram Athreya +2
Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process. The low signal-to-noise ratio and stochastic speckle pa…
Mentality: A Mamba-based Approach towards Foundation Models for EEG
Saarang Panchavati, Corey Arnold, William Speier
This work explores the potential of foundation models, specifically a Mamba-based selective state space model, for enhancing EEG analysis in neurological disorder diagnosis. EEG, c…
DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
Shreeram Athreya, Carlos Olivares, Ameera Ismail +3
Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, de…
CytoFM: The first cytology foundation model
Vedrana Ivezić, Ashwath Radhachandran, Ekaterina Redekop +5
Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is cha…
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
Ekaterina Redekop, Mara Pleasure, Vedrana Ivezic +5
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images. However, there is limited transparency into…
Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
Nithin Parthasarathy, James Soetedjo, Saarang Panchavati +4
Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, severely restricts patient communication capacity within a few years of onset, resulting in a…
Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model
Shreeram Athreya, Andrew Melehy, Sujit Silas Armstrong Suthahar +9
Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only…
High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training
Nithin Parthasarathy, James Soetedjo, Saarang Panchavati +4
Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300…