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Found 26 papers, 16 papers with code
Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation
CODA can be applied to new, unlabeled out-of-domain data sources of different sizes, from a single plate to multiple experimental batches.
Are Natural Domain Foundation Models Useful for Medical Image Classification?
The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks.
Privacy Protection in MRI Scans Using 3D Masked Autoencoders
Data anonymization and de-identification is concerned with ensuring the privacy and confidentiality of individuals' personal information.
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
In this paper, we propose a novel approach to TAMP that relaxes discrete-and-continuous TAMP problems into inference problems on a continuous domain.
Evaluation of large language models for discovery of gene set function
Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete.
Pretrained ViTs Yield Versatile Representations For Medical Images
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks.
Metadata-guided Consistency Learning for High Content Images
High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs.
Wide Range MRI Artifact Removal with Transformers
Our method is realized through the design of a novel volumetric transformer-based neural network that generalizes a \emph{window-centered} approach popularized by the Swin transformer.
PatchDropout: Economizing Vision Transformers Using Patch Dropout
This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size.
PSL is Dead. Long Live PSL
In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous domains.
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
Transfer learning is a standard technique to transfer knowledge from one domain to another.
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms.
Conditional De-Identification of 3D Magnetic Resonance Images
Solutions have been developed to de-identify diagnostic scans by obfuscating or removing parts of the face.
Should we Replace CNNs with Transformers for Medical Images?
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis, pushing the state-of-the-art in classification, detection and segmentation tasks.
Is it Time to Replace CNNs with Transformers for Medical Images?
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis.
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments.
Optimal asymptotic of the $J$ functional with respect to the $d_1$ metric
We obtain sharp inequalities between the large scale asymptotic of the $J$ functional with respect to the $d_1$ metric on the space of Kahler metrics.
Differential Geometry Complex Variables
Adversarial Privacy Preservation in MRI Scans of the Brain
De-identification of magnetic resonance imagery (MRI) is intrinsically difficult since, even with all metadata removed, a person's face can easily be rendered and matched against a database.
Unsupervised Discovery of 3D Physical Objects from Video
We study the problem of unsupervised physical object discovery.
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
With this in mind, we trained networks using three different criteria to select the positive training data (i. e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Visual relationship detection is fundamental for holistic image understanding.
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from developmental psychology.
An empirical study of the relation between network architecture and complexity
In this preregistration submission, we propose an empirical study of how networks handle changes in complexity of the data.
End-to-End Differentiable Physics for Learning and Control
We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning.
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
We show that training a deep network using batch normalization is equivalent to approximate inference in Bayesian models.
