Search Results for author:
Found 19 papers, 10 papers with code
Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology
Foundation models are rapidly being developed for computational pathology applications.
PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology
Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine.
Adapting Self-Supervised Learning for Computational Pathology
Self-supervised learning (SSL) has emerged as a key technique for training networks that can generalize well to diverse tasks without task-specific supervision.
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
An image-to-image translation strategy between imaging modalities is used to produce annotated pseudo-target volumes and improve generalization to the unannotated target modality.
Virchow: A Million-Slide Digital Pathology Foundation Model
The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
Ranked #1 on
Breast Tumour Classification
on PCam
(Accuracy metric, using extra
training data)
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervision
Then, by teaching the model to convert images across modalities, we leverage available pixel-level annotations from the source modality to enable segmentation in the unannotated target modality.
Label noise in segmentation networks : mitigation must deal with bias
In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary.
Towards annotation-efficient segmentation via image-to-image translation
Specifically, we propose a semi-supervised framework that employs unpaired image-to-image translation between two domains, presence vs. absence of cancer, as the unsupervised objective.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization.
Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies
Modelling long-term dependencies is a challenge for recurrent neural networks.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
On orthogonality and learning RNNs with long term dependencies
We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes.
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.
On orthogonality and learning recurrent networks with long term dependencies
We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.
The Importance of Skip Connections in Biomedical Image Segmentation
In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation.
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours.
