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Found 36 papers, 16 papers with code
Adaptive Prototype Learning for Multimodal Cancer Survival Analysis
Leveraging multimodal data, particularly the integration of whole-slide histology images (WSIs) and transcriptomic profiles, holds great promise for improving cancer survival prediction.
Scaling up self-supervised learning for improved surgical foundation models
Trained on the largest reported surgical dataset to date, comprising over 4. 7 million video frames, SurgeNetXL achieves consistent top-tier performance across six datasets spanning four surgical procedures and three tasks, including semantic segmentation, phase recognition, and critical view of safety (CVS) classification.
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles
Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance.
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine Segmentation
To this end, short-axis cine stacks of 296 subjects (90618 2D slices) were used for unlabeled pretraining with four SSP methods; SimCLR, positional contrastive learning, DINO, and masked image modeling (MIM).
Dataset Distribution Impacts Model Fairness: Single vs. Multi-Task Learning
We evaluate the performance of skin lesion classification using ResNet-based CNNs, focusing on patient sex variations in training data and three different learning strategies.
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
In particular, our model outperforms SAM by 4. 1 and 2. 5 percent points on a ductal carcinoma in situ (DCIS) segmentation tasks and breast cancer metastasis segmentation task (CAMELYON16 dataset).
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Effect of latent space distribution on the segmentation of images with multiple annotations
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Generalized Probabilistic U-Net for medical image segementation
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods.
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources.
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty
In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans.
Using uncertainty estimation to reduce false positives in liver lesion detection
We find that the use of a dropout rate of 0. 5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.
Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images
In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers.
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology
However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use.
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Therefore, we propose a detection and classification system for lung nodules in CT scans.
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks
A second DMEK expert annotated the test set to determine inter-rater performance.
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Ranked #5 on
Breast Tumour Classification
on PCam
Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study
We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC).
Liver segmentation and metastases detection in MR images using convolutional neural networks
Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome.
Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied.
Motion correction of dynamic contrast enhanced MRI of the liver
Compared to a pairwise method or no registration, groupwise registration provided better alignment.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Crowd disagreement about medical images is informative
Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds.
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging.
Inferring a Third Spatial Dimension from 2D Histological Images
Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast.
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter.
Domain-adversarial neural networks to address the appearance variability of histopathology images
Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters.
Adversarial training and dilated convolutions for brain MRI segmentation
To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations.
Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model, without the intermediate step of nuclei segmentation.
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer.
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers.
