BUSCADOR RECOLECTA

Project Description

Hyperspectral imaging and machine learning have been employed in the medical field for classifying highly infiltrative brain tumors. Although existing HSI databases of in-vivo human brains are available, they present two main deficiencies. Firstly, the amount of labeled data is scarce and secondly, 3D-tissue information is unavailable. To address both issues, we present the SLIMBRAIN database, a multimodal image database of in-vivo human brains which provides HS brain tissue data within the 400-1000 nm spectrum, as well as RGB, depth and multi-view images. Two HS cameras, two depth cameras and different RGB sensors were used to capture images and videos from 193 patients. All data in the SLIMBRAIN database can be used in a variety of ways, for example to train ML models with more than 1 million HS pixels available and labeled by neurosurgeons, to reconstruct 3D scenes or to visualize RGB brain images with different pathologies, offering unprecedented flexibility for both the medical and engineering communities.

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Data Description

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The SLIMBRAIN database contains anonymous hyperspectral, depth and RGB image data from in-vivo, and also ex-vivo, human brains from 193 patients.

SLIMBRAIN database. The available data are:

- CalibrationFiles: 5 .zip files to calibrate hyperspectral data for the different SLIMBRAIN prototypes and 1 .zip file containing the intrinsic and extrinsic parameters for some cameras.

- Datasets: 2 .zip files containing the patient's datasets for the snapshot and linescan hyperspectral cameras.

- GroundTruthMaps: 2 .zip files containing the patient's ground-truths folders for the snapshot and linescan hyperspectral cameras.

- PaperExperiments: 1 .zip files containing several files that store the patient IDs used for the results shown in the paper.

- preProcessedImages: Several .zip files containing the hyperspectral pre-processed cubes for the snapshot and linescan hyperspectral cameras.

- RawFiles: 193 .zip files containing the raw files acquired in the operating room for each of the 193 patients. These files contains the raw images from different cameras, videos and depth images.

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Notes

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To access the SLIMBRAIN Database, you need to fill, accept and sign the Data Usage Agreement terms. Then, you need to send it to us, using the emails included at the end of the document. We will evaluate your application and, if you are accepted, you will receive a confirmation email with the necessary steps to access the data.

You can either find the Data Usage Agreement within this page or at https://slimbrain.citsem.upm.es. Then, you could access https://slimbrain.citsem.upm.es/search to filter the patients using the available online service provided by Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM) and Fundación para la Investigación Biomédica del Hospital Universitario 12 de Octubre (FIBH12O).

You could also use https://slimbrain.citsem.upm.es/files to see the raw data online without the need of downloading it.

For further information, you can visit the official SLIMBRAIN database website at https://slimbrain.citsem.upm.es, where you can find Python software to manage the hyperspectral data provided.

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Files

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- CalibrationFiles:

These files store the calibration files necessary for the hyperspectral data and depth cameras.

Specifically, folders starting with a number indicate a hyperspectral calibration library with dark

and white references at different working distances and tilt angles:

- 1_Tripod_popoman: For the Ximea snapshot camera. Illumination done with the Dolan Jenner lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.

- 2_Prototype_laser: For the Ximea snapshot camera. Illumination done with the Dolan Jenner lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.

- 3_Protoype_lidar: For the Ximea snapshot and Headwall linescan cameras. Illumination done with the Dolan Jenner lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.

- 4_Prototype_lidar: For the Ximea snapshot and Headwall linescan cameras. Illumination done with the Osram lamp and ambient fluorescent lamps turned on. Obtained in the operating room when empty.

- 5_Prototype_Kinect: For the Ximea snapshot and Headwall linescan cameras. Illumination done with the International Light lamp and ambient fluorescent lamps turned off. Obtained in the laboratory.

Furthermore, the depth, RGB and HS sensor calibration files, including intrinsic, extrinsic and distortion

parameters, are included as .json files in DepthCameraCalibrationFiles.

- Datasets:

These files stores each patient dataset with the spectral information of every labelled pixel. These are obtained from the coordinates of its corresponding ground-truth map and pre-processed cube, which have been labeled by the neurosurgeons using a labelling tool based on the Spectral Angle Map (SAM) metric. Patient datasets are available for the Ximea snapshot and Headwall linescan hyperspectral cameras.

- GroundTruthMaps:

These files stores each patient ground-truth map labeled by the neurosurgeons. The labelling tool is based on the Spectral Angle Map (SAM) metric as already used in existing hyperspectral in-vivo human brain databases. Patient ground-truth maps are available for the Ximea snapshot and Headwall linescan hyperspectral cameras.

- PaperExperiments.zip:

Contains 2 .txt files with the patient's IDs used for the experiments shown in the paper.

- preProcessedImages:

These files stores each patient hyperspectral pre-processed cube. These are obtained from the raw data included in the RawFiles folder and the described pre-processing chain applied to them. Patient pre-processed cubes are available for the Ximea snapshot and Headwall linescan hyperspectral cameras.

- RawFiles:

These files stores the raw files obtained in each of the operations. It can include hyperspectral data, RGB data and depth information for each patient ID. All data is anonimized to keep the privacy of each human patient.

Description of the project

ASTOUND is an EIC funded project (No. 101071191) under the HORIZON-EIC-2021-PATHFINDERCHALLENGES-01 call.

The aim of the project is to develop an artificial conscious AI based on the Attention Schema Theory (AST) proposed by Michel Graziano. This theory proposes that consciousness arises from the brain's ability to create and maintain a simplified model of its own processing, particularly focusing attention on certain aspects of its internal and external environment.

The project entails creating an AI system capable of exhibiting consciousness-like behaviours by implementing principles from the AST. This involves constructing a model that simulates attentional processes, allowing the AI to prioritise and focus on relevant information while disregarding irrelevant stimuli.

The ASTOUND project will provide an Integrative Approach for Awareness Engineering to establish consciousness in machines, and targeting the following goals:

Develop an AI architecture for Artificial Consciousness based on the Attention Schema Theory (AST) through an internal model of the state of the attention.

Implement the proposed architecture into a contextually aware virtual agent and prove improved performance thanks to the Attention Schema; for instance, by providing coherent discussion, self-regulation, short-and-long term memory, personalisation capabilities.

Define novel ways to measure the presence and level of consciousness in both humans and machines.

Description of the dataset

The dataset includes synthetic dialogues in the art domain that can be used for training a chatbot to discuss artworks within a museum setting. Leveraging Large Language Models (LLMs), particularly ChatGPT, the dataset comprises over 13,000 dialogues generated using prompt-engineering techniques. The dialogues cover a wide range of user and chatbot behaviours, including expert guidance, tutoring, and handling toxic user interactions.

The ArtEmis dataset serves as a basis, containing emotion attributions and explanations for artworks sourced from the WikiArt website. From this dataset, 800 artworks were selected based on consensus among human annotators regarding elicited emotions, ensuring balanced representation across different emotions. However, an imbalance in art styles distribution was noted due to the emphasis on emotional balance.

Each dialogue is uniquely identified using a "DIALOGUE_ID", encoding information about the artwork discussed, emotions, chatbot behaviour, and more. The dataset is structured into multiple files for efficient navigation and analysis, including metadata, prompts, dialogues, and metrics.

Objective evaluation of the generated dialogues was conducted, focusing on profile discrimination, anthropic behaviour detection, and toxicity evaluation. Various syntactic and semantic-based metrics are employed to assess dialogue quality, along with sentiment and subjectivity analysis. Tools like the MS Azure Content Moderator API, Detoxify library and LlamaGuard aid in toxicity evaluation.

The dataset's conclusion highlights the need for further work to handle biases, enhance toxicity detection, and incorporate multimodal information and contextual awareness. Future efforts will focus on expanding the dataset with additional tasks and improving chatbot capabilities for diverse scenarios.