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Found 7 papers, 3 papers with code
MV-Swin-T: Mammogram Classification with Multi-view Swin Transformer
Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis.
Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches
Using deep learning techniques, we have developed a methodology which can detect different levels of abnormality (i. e., low, medium, high or no abnormality) in maize plants independently of their growth stage.
SWIN-SFTNet : Spatial Feature Expansion and Aggregation using Swin Transformer For Whole Breast micro-mass segmentation
We also incorporate a novel embedding loss that calculates similarities between linear feature embeddings of the encoder and decoder blocks.
ConnectedUNets++: Mass Segmentation from Whole Mammographic Images
Deep learning has made a breakthrough in medical image segmentation in recent years due to its ability to extract high-level features without the need for prior knowledge.
Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms
Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world.
Resource Efficient Mountainous Skyline Extraction using Shallow Learning
We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions.
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection
Each of the method is tested on an extensive test set (about 3K images) covering various challenging geographical, weather, illumination and seasonal conditions.
