Search Results for author:
Found 37 papers, 19 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.
Data-Centric Machine Learning for Geospatial Remote Sensing Data
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
Certification of Deep Learning Models for Medical Image Segmentation
In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models.
On the detection of Out-Of-Distribution samples in Multiple Instance Learning
While significant efforts have been devoted to OOD detection in classical supervised settings, the context of weakly supervised learning, particularly the Multiple Instance Learning (MIL) framework, remains under-explored.
Multiple Instance Learning
Out of Distribution (OOD) Detection
+1
Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy
In particular, PDOs are attracting interest in the field of Functional Precision Medicine (FPM), which is based upon an ex-vivo drug test in which living tumor cells (such as PDOs) from a specific patient are exposed to a panel of anti-cancer drugs.
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.
Structured State Space Models for Multiple Instance Learning in Digital Pathology
In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified.
Towards Better Certified Segmentation via Diffusion Models
The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy.
MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation
Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently.
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.
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization
In Self-Supervised Learning (SSL), models are typically pretrained, fine-tuned, and evaluated on the same domains.
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.
Artifact Removal in Histopathology Images
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis.
Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but not in the full database.
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells.
Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses.
Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology
In this paper, to enhance robustness on unseen target protocols, we propose a new test-time data augmentation based on multi domain image-to-image translation.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field.
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment
In this paper, we propose a deep reinforcement learning method for accurate localization of the L3 CT slice.
Exploring Deep Registration Latent Spaces
Explainability of deep neural networks is one of the most challenging and interesting problems in the field.
Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization
Multi-Source Unsupervised Domain Adaptation (multi-source UDA) aims to learn a model from several labeled source domains while performing well on a different target domain where only unlabeled data are available at training time.
Contrastive Learning
Multi-Source Unsupervised Domain Adaptation
+1
A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification
To tackle these problems, we propose a novel spatial and magnification based attention sampling strategy.
Weakly supervised pan-cancer segmentation tool
The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expert variability.
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is the preferred approach for whole slide image classification.
Cancer Gene Profiling through Unsupervised Discovery
Precision medicine is a paradigm shift in healthcare relying heavily on genomics data.
Deep learning based registration using spatial gradients and noisy segmentation labels
Image registration is one of the most challenging problems in medical image analysis.
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
We show that the identification of the magnification level for tiles can generate a preliminary self-supervision signal to locate nuclei.
AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity.
Weakly supervised multiple instance learning histopathological tumor segmentation
In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems.
Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation
Image registration and segmentation are the two most studied problems in medical image analysis.
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
\begin{abstract} The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics.
U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets
We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Image registration and in particular deformable registration methods are pillars of medical imaging.
