22 citations · 36 across the 6 of their papers we have counts for
6 papers
Match Them Up: Visually Explainable Few-shot Image Classification
Bowen Wang, Liangzhi Li, Manisha Verma +3
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to nove…
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Liangzhi Li, Manisha Verma, Bowen Wang +3
The status of retinal arteriovenous crossing is of great significance for clinical evaluation of arteriolosclerosis and systemic hypertension. As an ophthalmology diagnostic criter…
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Bowen Wang, Liangzhi Li, Yuta Nakashima +3
Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolut…
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Liangzhi Li, Bowen Wang, Manisha Verma +3
Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not…
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Liangzhi Li, Manisha Verma, Yuta Nakashima +2
Retinal imaging serves as a valuable tool for diagnosis of various diseases. However, reading retinal images is a difficult and time-consuming task even for experienced specialists…
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Liangzhi Li, Manisha Verma, Yuta Nakashima +2
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new mo…