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Found 18 papers, 5 papers with code
Leveraging Disease Progression Learning for Medical Image Recognition
Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning.
Case-Based Histopathological Malignancy Diagnosis using Convolutional Neural Networks
In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined.
Dual Adversarial Inference for Text-to-Image Synthesis
), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).
FoCL: Feature-Oriented Continual Learning for Generative Models
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL).
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Learning in non-stationary environments is one of the biggest challenges in machine learning.
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective.
Ranked #1 on
Unsupervised Domain Adaptation
on Office-Home
(Avg accuracy metric)
Conditional Generation of Medical Images via Disentangled Adversarial Inference
Current practices in using cGANs for medical image generation, only use a single variable for image generation (i. e., content) and therefore, do not provide much flexibility nor control over the generated image.
Hypothesis Disparity Regularized Mutual Information Maximization
We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA) -- where the knowledge from a source domain is transferred solely through hypotheses and adapted to the target domain in an unsupervised manner.
Test-Time Adaptation and Adversarial Robustness
On the positive side, we show that, if one is allowed to access the training data, then Domain Adversarial Neural Networks (${\sf DANN}$), an algorithm designed for unsupervised domain adaptation, can provide nontrivial robustness in the test-time maximin threat model against strong transfer attacks and adaptive fixed point attacks.
Towards Adversarial Robustness via Transductive Learning
Compared to traditional "test-time" defenses, these defense mechanisms "dynamically retrain" the model based on test time input via transductive learning; and theoretically, attacking these defenses boils down to bilevel optimization, which seems to raise the difficulty for adaptive attacks.
FlowX: Towards Explainable Graph Neural Networks via Message Flows
We investigate the explainability of graph neural networks (GNNs) as a step toward elucidating their working mechanisms.
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images.
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes
As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations.
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models
To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives.
Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition
Overall, our work highlights the potential of MAE for ultrasound image recognition and presents a novel approach that incorporates deblurring to further improve its effectiveness.
Increasing SAM Zero-Shot Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
This study develops and evaluates a novel multimodal medical image zero-shot segmentation algorithm named Text-Visual-Prompt SAM (TV-SAM) without any manual annotations.
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.
