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Found 14 papers, 11 papers with code
Few-shot Adaptation of Medical Vision-Language Models
Integrating image and text data through multi-modal learning has emerged as a new approach in medical imaging research, following its successful deployment in computer vision.
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction.
Attention to detail: inter-resolution knowledge distillation
The development of computer vision solutions for gigapixel images in digital pathology is hampered by significant computational limitations due to the large size of whole slide images.
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models
Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples.
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Digital pathology has become a standard in the pathology workflow due to its many benefits.
Towards foundation models and few-shot parameter-efficient fine-tuning for volumetric organ segmentation
With the recent raise of foundation models in computer vision and NLP, the pretrain-and-adapt strategy, where a large-scale model is fine-tuned on downstream tasks, is gaining popularity.
Challenging mitosis detection algorithms: Global labels allow centroid localization
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers.
Constrained unsupervised anomaly segmentation
In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.
Looking at the whole picture: constrained unsupervised anomaly segmentation
In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net
Prostate cancer is one of the most prevalent cancers worldwide.
Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection
The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies.
WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images
Regarding the estimation of the core-level Gleason score, we obtained a k of 0. 76 and 0. 67 between the model and two different pathologists.
Self-learning for weakly supervised Gleason grading of local patterns
Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (k) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task.
Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
Worldwide, prostate cancer is one of the main cancers affecting men.
