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Found 13 papers, 1 papers with code
PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation
Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity.
PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation
Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning.
DECO: Query-Based End-to-End Object Detection with ConvNets
The proposed framework, \ie, Detection ConvNet (DECO), is composed of a backbone and convolutional encoder-decoder architecture.
Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing
First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions.
Statistical Attention Localization (SAL): Methodology and Application to Object Classification
SAL consists of three steps: 1) preliminary attention window selection via decision statistics, 2) attention map refinement, and 3) rectangular attention region finalization.
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking
The design of robust learning systems that offer stable performance under a wide range of supervision degrees is investigated in this work.
Subspace Learning Machine (SLM): Methodology and Performance
The partitioning process is recursively applied at each child node to build an SLM tree.
HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation
The second stage exploits the segmentation masks obtained in the first stage and leverages color and shape distributions for a more accurate segmentation.
On Supervised Feature Selection from High Dimensional Feature Spaces
The application of machine learning to image and video data often yields a high dimensional feature space.
Unsupervised Data-Driven Nuclei Segmentation For Histology Images
An unsupervised data-driven nuclei segmentation method for histology images, called CBM, is proposed in this work.
E-PixelHop: An Enhanced PixelHop Method for Object Classification
Forth, pixel-level decisions from each hop and from each color subspace are fused together for image-level decision.
Semi-supervised learning via Feedforward-Designed Convolutional Neural Networks
A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work.
Ensembles of feedforward-designed convolutional neural networks
An ensemble method that fuses the output decision vectors of multiple feedforward-designed convolutional neural networks (FF-CNNs) to solve the image classification problem is proposed in this work.
