CSMed wound dressing for prophylaxis and management of radiation dermatitis in breast and head-neck cancer patients: a single hospital prospective clinical trial.

Purpose: CSMed wound dressing, a dressing with various herb extracts, was tested for its therapeutic effect in radiation dermatitis of breast and head-and-neck cancer patients.

Methods: This study included 20 breast cancer patients and 10 head-and-neck cancer patients. Half of the irradiated area was covered with CSMed and the other half was under routine treatment.

Learning Binary Hash Codes Based on Adaptable Label Representations.

The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing.

Cross-Batch Reference Learning for Deep Retrieval.

Learning effective representations that exhibit semantic content is crucial to image retrieval applications. Recent advances in deep learning have made significant improvements in performance on a number of visual recognition tasks. Studies have also revealed that visual features extracted from a deep network learned on a large-scale image data set (e.

Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks.

This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties.

Feature selection for inductive generalization.

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features.