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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Continual Domain Incremental Learning for Privacy-aware Digital Pathology
Pratibha Kumari, Daniel Reisenbüchler, Lucas Luttner +3
In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithm…
SLA-INR: Single-Layer Learnable Activation for Implicit Neural Representation
Moein Heidari, Reza Rezaeian, Reza Azad +3
Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several v…
Tissue Concepts: supervised foundation models in computational pathology
Till Nicke, Jan Raphael Schaefer, Henning Hoefener +4
Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Founda…
Physics-Inspired Generative Models in Medical Imaging: A Review
Dennis Hein, Afshin Bozorgpour, Dorit Merhof +1
Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging…
Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study
Yousef Sadegheih, Dorit Merhof
Brain metastasis segmentation poses a significant challenge in medical imaging due to the complex presentation and variability in size and location of metastases. In this study, we…
Unsupervised Latent Stain Adaptation for Computational Pathology
Daniel Reisenbüchler, Lucas Luttner, Nadine S. Schaadt +2
In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techn…
MSANet: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation
Sina Ghorbani Kolahi, Seyed Kamal Chaharsooghi, Toktam Khatibi +5
Medical image segmentation involves identifying and separating object instances in a medical image to delineate various tissues and structures, a task complicated by the significan…
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
Moein Heidari, Sina Ghorbani Kolahi, Sanaz Karimijafarbigloo +8
Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emerge…
LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation
Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari +2
The rise of Transformer architectures has advanced medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers. However,…
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Moein Heidari, Reza Azad, Sina Ghorbani Kolahi +8
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Comput…