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Found 34 papers, 12 papers with code
Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
In contrast to traditional concept-based interpretable models, our approach eliminates the need for costly human annotations by leveraging the vision-language model.
Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications
Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity.
Masked Attention as a Mechanism for Improving Interpretability of Vision Transformers
Vision Transformers are at the heart of the current surge of interest in foundation models for histopathology.
Uncertainty-guided annotation enhances segmentation with the human-in-the-loop
Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use.
Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images
Vision Transformers (ViTs) have ushered in a new era in computer vision, showcasing unparalleled performance in many challenging tasks.
Medical diffusion on a budget: Textual Inversion for medical image generation
This work shows that adapting pre-trained Stable Diffusion models to medical imaging modalities is achievable by training text embeddings using Textual Inversion.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Automatic tumour segmentation in H&E-stained whole-slide images of the pancreas
A crucial first step in these pipelines is typically identification and segmentation of the tumour area.
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.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels
As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field.
Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
We investigated the value of AI assistance for grading prostate biopsies.
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
Furthermore, we show that the meta-loss function of M-heads improves OOD detection in terms of AUROC from 87. 4 to 88. 7.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image.
Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
Automated medical image segmentation plays a key role in quantitative research and diagnostics.
Automated Gleason Grading of Prostate Biopsies using Deep Learning
We developed a fully automated deep learning system to grade prostate biopsies.
Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification
Results show that semi-/unsupervised methods have an advantage over supervised learning when few labels are available.
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.
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance.
Neural Image Compression for Gigapixel Histopathology Image Analysis
Second, a convolutional neural network (CNN) is trained on these compressed image representations to predict image-level labels, avoiding the need for fine-grained manual annotations.
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs).
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
The H&E slides were co-registered to the IHC slides.
Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders
We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data.
Training convolutional neural networks with megapixel images
To train deep convolutional neural networks, the input data and the intermediate activations need to be kept in memory to calculate the gradient descent step.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area.
Comparison of Different Methods for Tissue Segmentation in Histopathological Whole-Slide Images
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology.
The importance of stain normalization in colorectal tissue classification with convolutional networks
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
