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Found 12 papers, 3 papers with code
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
We propose fusing both modalities using a memory-efficient multimodal Transformer that can model interactions between pathway and histology patch tokens.
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
The recent explosion of interest in multimodal applications has resulted in a wide selection of datasets and methods for representing and integrating information from different modalities.
Fuzzy Dark Matter and the 21cm Power Spectrum
If FDM constitutes the entirety of the dark matter and the FDM particle mass is 10-21eV, HERA can determine the mass to within 20 percent at 2-sigma confidence.
Cosmology and Nongalactic Astrophysics Computational Physics
COVID-19 in differential diagnosis of online symptom assessments
Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today.
A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest.
Learning Montezuma's Revenge from a Single Demonstration
We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge.
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
However, CNNs require large amounts of labeled histopathology data.
Rethinking Monocular Depth Estimation with Adversarial Training
Most existing approaches treat depth estimation as a regression problem with a local pixel-wise loss function.
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images
Our experiments demonstrate that: (a) Convolutional Neural Networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model.
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training
We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization.
Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Metastatic presence in lymph nodes is one of the most important prognostic variables of breast cancer.
