20 citations · 42 across the 13 of their papers we have counts for
8 papers · 1 filter
GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology
Saarthak Kapse, Pushpak Pati, Srikar Yellapragada +5
Pretraining a Multiple Instance Learning (MIL) aggregator enables the derivation of Whole Slide Image (WSI)-level embeddings from patch-level representations without supervision. W…
TopoDiffusionNet: A Topology-aware Diffusion Model
Saumya Gupta, Dimitris Samaras, Chao Chen
Diffusion models excel at creating visually impressive images but often struggle to generate images with a specified topology. The Betti number, which represents the number of stru…
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-…
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…
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…
Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
Jingwei Zhang, Xin Zhang, Ke Ma +4
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tool…