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Found 7 papers, 1 papers with code
Improving Bird's Eye View Semantic Segmentation by Task Decomposition
In the first stage, we train a BEV autoencoder to reconstruct the BEV segmentation maps given corrupted noisy latent representation, which urges the decoder to learn fundamental knowledge of typical BEV patterns.
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
In this way, the model could access more variant data samples of an instance and keep predicting invariant discriminative representations for them.
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which can be automatically applied to each cancer type without human adjustment.
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis.
3D Facial Expression Reconstruction using Cascaded Regression
2D landmarks are detected and used to initialize the 3D shape and mapping matrices.
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images.
