Search Results for author:
Found 10 papers, 5 papers with code
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides.
Learned representation-guided diffusion models for large-image generation
Generating images from learned embeddings is agnostic to the source of the embeddings.
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging.
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
On these two datasets, the proposed additional pathology foundation model further achieves a relative improvement of 5. 07% to 5. 12% in Dice score and 4. 50% to 8. 48% in IOU.
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images.
Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning
Compared to conventional full fine-tuning approaches, we fine-tune less than 1. 3% of the parameters, yet achieve a relative improvement of 1. 29%-13. 61% in accuracy and 3. 22%-27. 18% in AUROC and reduce GPU memory consumption by 38%-45% while training 21%-27% faster.
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
Our method outperforms previous dense matching methods by up to 7. 2% in average precision for detection and 5. 6% in average precision for instance segmentation tasks.
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network
To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net).
TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures.
Predicting Clinical Outcomes in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study
Radiomic and DL classification models had mAUCs of 0. 78+/-0. 02 and 0. 81+/-0. 04, compared with expert scores mAUCs of 0. 75+/-0. 02 and 0. 79+/-0. 05 for mechanical ventilation requirement and mortality prediction, respectively.
