9 papers with code • 1 benchmarks • 1 datasets
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Ranked #2 on Semantic Object Interaction Classification on VLOG
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
Ranked #1 on Pedestrian Attribute Recognition on UAV-Human
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
Ranked #7 on Breast Tumour Classification on PCam
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.
Ranked #6 on Breast Tumour Classification on PCam
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
Ranked #1 on Colorectal Gland Segmentation: on CRAG
This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Ranked #5 on Breast Tumour Classification on PCam
In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.