Abstract: We present a new skin modeling technique based on SNoW (sparse network of Winnows) for accurate and robust skin region detection.

The main advantage of using the SDM method over the SPM method is that the complexity, memory requirements and time for skin detection are reduced ...

A skin distribution map (SDM) representing the sparse network is trained with skin pixels to learn their distribution in a color space. We then train the SDM ...

May 2, 2022 · In this paper, we propose a two-stage segmentation pipeline using coarse and sparse annotations on a small region of the whole slide image as the training set.

In this paper, we attempt to tackle the skin biopsy image feature extraction problem under a recently proposed machine learning framework, multi-instance ...

In our studies, we focus on the research and applications of sparse representation based algorithms for image classification including but not limited to faces, ...

Feb 21, 2022 · This study investigates the feasibility of using few-shot learning-based techniques to mitigate the data requirements for accurate training.

Aug 2, 2024 · This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis.

When training data is sparse, more domain knowledge must be incorporated into the learn- ing algorithm in order to reduce the effective size.