37 citations · 130 across the 18 of their papers we have counts for
11 papers · 1 filter
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao +27
Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning trans…
Time-to-Event Pretraining for 3D Medical Imaging
Zepeng Huo, Jason Alan Fries, Alejandro Lozano +6
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predic…
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37
The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…
Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation
Tanvi Deshpande, Eva Prakash, Elsie Gyang Ross +3
The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to tra…
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M Patel
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation. For most ap…
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M. Patel
Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backb…
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
Zhihong Chen, Maya Varma, Justin Xu +20
Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant…
CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition
Deepti Hegde, Jeya Maria Jose Valanarasu, Vishal M. Patel
Vision-Language models like CLIP have been widely adopted for various tasks due to their impressive zero-shot capabilities. However, CLIP is not suitable for extracting 3D geometri…
Ambiguous Medical Image Segmentation using Diffusion Models
Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu +1
Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existin…
Target and Task specific Source-Free Domain Adaptive Image Segmentation
Vibashan VS, Jeya Maria Jose Valanarasu, Vishal M. Patel
Solving the domain shift problem during inference is essential in medical imaging, as most deep-learning based solutions suffer from it. In practice, domain shifts are tackled by p…
Overcomplete Representations Against Adversarial Videos
Shao-Yuan Lo, Jeya Maria Jose Valanarasu, Vishal M. Patel
Adversarial robustness of deep neural networks is an extensively studied problem in the literature and various methods have been proposed to defend against adversarial images. Howe…