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Found 17 papers, 8 papers with code
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Segmentation of 3D images is a fundamental problem in biomedical image analysis.
Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks
In this work, we design a new deep neural network model which extends the fully convolutional network by combining the ideas in several recent techniques, such as shortcut links.
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
The high computation throughput and memory bandwidth of graphics processing units (GPUs) make GPUs a natural choice for accelerating convolution operations.
Neuron Segmentation Using Deep Complete Bipartite Networks
We propose a new FCN-type deep learning model, called deep complete bipartite networks (CB-Net), and a new scheme for leveraging approximate instance-wise annotation to train our pixel-wise prediction model.
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences.
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.
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.
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
MMV_Im2Im: An Open Source Microscopy Machine Vision Toolbox for Image-to-Image Transformation
For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods.
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.
EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency and Representation
Artificial intelligence (AI) has been widely used in bioimage image analysis nowadays, but the efficiency of AI models, like the energy consumption and latency is not ignorable due to the growing model size and complexity, as well as the fast-growing analysis needs in modern biomedical studies.
Masked Diffusion as Self-supervised Representation Learner
Denoising diffusion probabilistic models have recently demonstrated state-of-the-art generative performance and have been used as strong pixel-level representation learners.
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Medical Image Segmentation
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MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Deep learning based Image Compression for Microscopy Images: An Empirical Study
In the end, we hope the present study could shed light on the potential of deep learning based image compression and the impact of image compression on downstream deep learning based image analysis models.
Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images
In this paper, we study the morphology of brain vasculature through a topological lens.
