148 research outputs found
Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods are discriminative
approaches, which model the conditional distribution of labels given inputs
features. The success of such approaches heavily depends on high-quality
labeled instances, which are not easy to obtain, especially as the number of
candidate classes increases. In this paper, we study the complementary learning
problem. Unlike ordinary labels, complementary labels are easy to obtain
because an annotator only needs to provide a yes/no answer to a randomly chosen
candidate class for each instance. We propose a generative-discriminative
complementary learning method that estimates the ordinary labels by modeling
both the conditional (discriminative) and instance (generative) distributions.
Our method, we call Complementary Conditional GAN (CCGAN), improves the
accuracy of predicting ordinary labels and can generate high-quality instances
in spite of weak supervision. In addition to the extensive empirical studies,
we also theoretically show that our model can retrieve the true conditional
distribution from the complementarily-labeled data
Effective Drusen Localization for Early AMD Screening using Sparse Multiple Instance Learning
Age-related Macular Degeneration (AMD) is one of the leading causes of blindness. Automatic screening of AMD has attracted much research effort in recent years because it brings benefits to both patients and ophthalmologists. Drusen is an important clinical indicator for AMD in its early stage. Accurately detecting and localizing drusen are important for AMD detection and grading. In this paper, we propose an effective approach to localize drusen in fundus images. This approach trains a drusen classifier from a weakly labeled dataset, i.e., only the existence of drusen is known but not the exact locations or boundaries, by employing Multiple Instance Learning (MIL). Specifically, considering the sparsity of drusen in fundus images, we employ sparse Multiple Instance Learning to obtain better performance compared with classical MIL. Experiments on 350 fundus images with 96 having AMD demonstrates that on the task of AMD detection, multiple instance learning, both classical and sparse versions, achieve comparable performance compared with fully supervised SVM. On the task of drusen localization, sparse MIL outperforms MIL significantly
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
This paper introduces an innovative methodology for producing high-quality 3D
lung CT images guided by textual information. While diffusion-based generative
models are increasingly used in medical imaging, current state-of-the-art
approaches are limited to low-resolution outputs and underutilize radiology
reports' abundant information. The radiology reports can enhance the generation
process by providing additional guidance and offering fine-grained control over
the synthesis of images. Nevertheless, expanding text-guided generation to
high-resolution 3D images poses significant memory and anatomical
detail-preserving challenges. Addressing the memory issue, we introduce a
hierarchical scheme that uses a modified UNet architecture. We start by
synthesizing low-resolution images conditioned on the text, serving as a
foundation for subsequent generators for complete volumetric data. To ensure
the anatomical plausibility of the generated samples, we provide further
guidance by generating vascular, airway, and lobular segmentation masks in
conjunction with the CT images. The model demonstrates the capability to use
textual input and segmentation tasks to generate synthesized images. The
results of comparative assessments indicate that our approach exhibits superior
performance compared to the most advanced models based on GAN and diffusion
techniques, especially in accurately retaining crucial anatomical features such
as fissure lines, airways, and vascular structures. This innovation introduces
novel possibilities. This study focuses on two main objectives: (1) the
development of a method for creating images based on textual prompts and
anatomical components, and (2) the capability to generate new images
conditioning on anatomical elements. The advancements in image generation can
be applied to enhance numerous downstream tasks
Learning to screen Glaucoma like the ophthalmologists
GAMMA Challenge is organized to encourage the AI models to screen the
glaucoma from a combination of 2D fundus image and 3D optical coherence
tomography volume, like the ophthalmologists
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