390 citations · 472 across the 32 of their papers we have counts for
17 papers · 1 filter
BigCell: Generating Gigapixel Whole-Slide Images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +10
Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models…
RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models
Moinak Bhattacharya, Gagandeep Singh, Shubham Jain +1
In this work, we present RadGazeGen, a novel framework for integrating experts' eye gaze patterns and radiomic feature maps as controls to text-to-image diffusion models for high f…
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Saarthak Kapse, Pushpak Pati, Srijan Das +7
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slid…
Learned representation-guided diffusion models for large-image generation
Alexandros Graikos, Srikar Yellapragada, Minh-Quan Le +4
To synthesize high-fidelity samples, diffusion models typically require auxiliary data to guide the generation process. However, it is impractical to procure the painstaking patch-…
Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy
Yunfan Li, Himanshu Gupta, Haibin Ling +4
Cholecystectomy (gallbladder removal) is one of the most common procedures in the US, with more than 1.2M procedures annually. Compared with classical open cholecystectomy, laparos…
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Saarthak Kapse, Srijan Das, Jingwei Zhang +4
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive app…
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada, Alexandros Graikos, Prateek Prasanna +3
To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathol…
Topology-Aware Uncertainty for Image Segmentation
Saumya Gupta, Yikai Zhang, Xiaoling Hu +2
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelera…
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
Xuan Xu, Saarthak Kapse, Rajarsi Gupta +1
Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial…
Token Sparsification for Faster Medical Image Segmentation
Lei Zhou, Huidong Liu, Joseph Bae +3
Can we use sparse tokens for dense prediction, e.g., segmentation? Although token sparsification has been applied to Vision Transformers (ViT) to accelerate classification, it is s…
Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning
Jingwei Zhang, Saarthak Kapse, Ke Ma +4
Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-lear…
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
Aishik Konwer, Xiaoling Hu, Joseph Bae +3
In medical vision, different imaging modalities provide complementary information. However, in practice, not all modalities may be available during inference or even training. Prev…
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
Jingwei Zhang, Saarthak Kapse, Ke Ma +4
Dense prediction tasks such as segmentation and detection of pathological entities hold crucial clinical value in computational pathology workflows. However, obtaining dense annota…
Learning Topological Interactions for Multi-Class Medical Image Segmentation
Saumya Gupta, Xiaoling Hu, James Kaan +10
Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions…
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Yunfan Li, Vinayak Shenoy, Prateek Prasanna +3
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. In this report, we propose a Transformer-based method that utilizes…
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network
Saarthak Kapse, Srijan Das, Prateek Prasanna
Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large field of view, i.e more contexual…
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification
Moinak Bhattacharya, Shubham Jain, Prateek Prasanna
In this work, we present RadioTransformer, a novel visual attention-driven transformer framework, that leverages radiologists' gaze patterns and models their visuo-cognitive behavi…