Our solution for BreastPathQ cancer cellularity challenge
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Updated
Apr 28, 2019
Our solution for BreastPathQ cancer cellularity challenge
The nuclei detection method on histology image proposed in the 2017 paper by Peikari et al.
A speedy Android app system that efficiently delivers diagnostic information written by pathologists to their fellow doctors. Won First Prize at IETE's Inter-college Competition from amongst 130+ teams
Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning
Scripts for https://www.nature.com/articles/s41598-018-27707-4, using Convolutional Neural Network to detect lung cancer tumor area
Python library for pathology image analysis
LabelSlide is a slide annotation tool and label object bounding boxes in virtual slides (generally used in pathology)
A multi-resolution CNN to predict endometrial cancer features
A multi-resolution CNN package to predict endometrial cancer features
BIRL: Benchmark on Image Registration methods with Landmark validations
Contextual Attention Network: Transformer Meets U-Net
Noise Robust Learning with Hard Example Aware for Pathological Image classification
🔬 Syntax - the arrangement of whole-slide-images and their image tiles to create well-formed computational pathology pipelines.
Simple prototype of converting whole slide image (WSI) to multiframe DICOM images
Image data augmentation on-the-fly by add new class on transforms in PyTorch and torchvision.
Creating a Crowd sourcing platform to label pathological images. These images are first downloaded from internet using Twitter APIs and then passed through a deep learning model which filter or separate 'Pathological' & 'Non-Pathological' Images.
BrainPainter - Brain Visualisation Software
The code for Kernel attention transformer (KAT)
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