Search Results for author:
Found 8 papers, 5 papers with code
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R$^2$T is tailored to re-embed instance features online.
Review of AlexNet for Medical Image Classification
In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification.
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Moreover, the student is used to update the teacher with an exponential moving average (EMA), which in turn identifies new hard instances for subsequent training iterations and stabilizes the optimization.
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets.
Data-driven spatio-temporal modelling of glioblastoma
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels.
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification
To overcome this drawback, we present a \textit{Patch Refiner} to cluster patches into different groups and only select the highest distress-risk group to yield a slim head for the final image classification.
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild
Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information.
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes.
