Dataset Information

Geometrically restricted image descriptors: A method to capture the appearance of shape.

ABSTRACT: Shape perception varies depending on many factors. For example, presenting a stimulus in the periphery often yields a different appearance compared with its foveal presentation. However, how exactly shape appearance is altered under different conditions remains elusive. One reason for this is that studies typically measure identification performance, leaving details about target appearance unknown. The lack of appearance-based methods and general challenges to quantify appearance complicate the investigation of shape appearance. Here, we introduce Geometrically Restricted Image Descriptors (GRIDs), a method to investigate the appearance of shapes. Stimuli in the GRID paradigm are shapes consisting of distinct line elements placed on a grid by connecting grid nodes. Each line is treated as a discrete target. Observers are asked to capture target appearance by placing lines on a freely viewed response grid. We used GRIDs to investigate the appearance of letters and letter-like shapes. Targets were presented at 10° eccentricity in the right visual field. Gaze-contingent stimulus presentation was used to prevent eye movements to the target. The data were analyzed by quantifying the differences between targets and response in regard to overall accuracy, element discriminability, and several distinct error types. Our results show how shape appearance can be captured by GRIDs, and how a fine-grained analysis of stimulus parts provides quantifications of appearance typically not available in standard measures of performance. We propose that GRIDs are an effective tool to investigate the appearance of shapes.

SUBMITTER: Melnik N

PROVIDER: S-EPMC7961119 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionCancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at the patch level are more computationally attractive. Such methodologies capitalize on pixel level annotations (tissue delineations) from expert pathologists, which are then used to derive labels at the patch level. In this work, we envision a digital connected health system that augments the capabilities of the clinicians by providing powerful feature descriptors that may describe malignant regions.Material and methodsWe start with a patch level descriptor, termed Covariance-Kernel Descriptor (CKD), capable of compactly describing tissue architectures associated with carcinomas. To leverage the recognition capability of the CKDs to larger slide regions, we resort to a multiple instance learning framework. In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. The WAID is computed on bags of patches corresponding to larger image regions for which binary labels (malignant vs. benign) are provided, thus obviating the necessity for tissue delineations.ResultsThe CKD was seen to outperform all the considered descriptors, reaching classification accuracy (ACC) of 92.83%. and area under the curve (AUC) of 0.98. The CKD captures higher order correlations between features and was shown to achieve superior performance against a large collection of computer vision features on a private breast cancer dataset. The WAID outperform all other descriptors on the Breast Cancer Histopathological database (BreakHis) where correctly classified malignant (CCM) instances reached 91.27 and 92.00% at the patient and image level, respectively, without resorting to a deep learning scheme achieves state-of-the-art performance.DiscussionOur proposed derivation of the CKD and WAID can help medical experts accomplish their work accurately and faster than the current state-of-the-art.

Dataset Information

Geometrically restricted image descriptors: A method to capture the appearance of shape.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets