Search Results for author:
Found 10 papers, 8 papers with code
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability.
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease.
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children.
CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification
Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems.
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images
Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are sub-optimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection.
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i. e., healthy) images to detect any abnormal (i. e., unhealthy) samples that do not conform to the expected normal patterns.
Ranked #1 on
Anomaly Detection
on LAG
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos.
Anomaly Detection In Surveillance Videos
Contrastive Learning
+2
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos.
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Anomaly detection methods generally target the learning of a normal image distribution (i. e., inliers showing healthy cases) and during testing, samples relatively far from the learned distribution are classified as anomalies (i. e., outliers showing disease cases).
Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames
We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.
