Unsupervised anomaly detection in medical images such as chest radiograp...
There is a strong incentive to develop computational pathology models to...
With the rise of open data, identifiability of individuals based on 3D
r...
Monte-Carlo diffusion simulations are a powerful tool for validating tis...
This paper presents an efficient online framework to solve the well-know...
Most self-supervised methods for representation learning leverage a
cros...
Learning dense visual representations without labels is an arduous task ...
Most recent test-time adaptation methods focus on only classification ta...
Screening Papanicolaou test samples effectively reduces cervical
cancer-...
Designing Deep Neural Networks (DNNs) running on edge hardware remains a...
Progress in digital pathology is hindered by high-resolution images and ...
Purpose: To investigate aspects of the validation of self-supervised
alg...
The current multiple sclerosis (MS) diagnostic criteria lack specificity...
The spatial impulse response (SIR) method is a well-known approach to
ca...
In medical image segmentation, supervised deep networks' success comes a...
Supervised learning is constrained by the availability of labeled data, ...
Existing reference (RF)-based super-resolution (SR) models try to improv...
Despite the successes of deep neural networks on many challenging vision...
Segmenting histology images into diagnostically relevant regions is
impe...
Cancer diagnosis and prognosis for a tissue specimen are heavily influen...
Detecting anomalies in musculoskeletal radiographs is of paramount impor...
Explainability of deep learning methods is imperative to facilitate thei...
Deep anomaly detection models using a supervised mode of learning usuall...
Thanks to its capability of acquiring full-view frames at multiple kiloh...
Ultrafast ultrasound (US) revolutionized biomedical imaging with its
cap...
The automated detection of cortical lesions (CLs) in patients with multi...
Although generative adversarial network (GAN) based style transfer is st...
With the long-term rapid increase in incidences of colorectal cancer (CR...
Super-resolution (SR) has traditionally been based on pairs of
high-reso...
Cancer diagnosis, prognosis, and therapeutic response prediction are hea...
Explainability of machine learning (ML) techniques in digital pathology ...
Medical image segmentation is an important task for computer aided diagn...
Most of the existing deep neural nets on automatic facial expression
rec...
By benefiting from perceptual losses, recent studies have improved
signi...
Despite significant progress toward super resolving more realistic image...
State-of-the-art deep face recognition approaches report near perfect
pe...
In this paper, we present a new dataset for Form Understanding in Noisy
...
Cross-domain synthesizing realistic faces to learn deep models has attra...
Attribute guided face image synthesis aims to manipulate attributes on a...
The ability of a graph neural network (GNN) to leverage both the graph
t...
We introduce a new scene graph generation method called image-level
atte...
In this work, we present a comparison of a shallow and a deep learning
a...
Training robust deep learning (DL) systems for medical image classificat...
Despite significant advances in improving the gaze estimation accuracy u...
Littering quantification is an important step for improving cleanliness ...
Visual speech recognition is a challenging research problem with a parti...
Automatic visual speech recognition is an interesting problem in pattern...
Pedestrian detection is an important component for safety of autonomous
...
We propose a compressive sensing algorithm that exploits geometric prope...