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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Genetic Lingo

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Improving Translational Accuracy

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Vision-Language Transformer for Interpretable Pathology Visual Question Answering.

Usman Naseem, Matloob Khushi, Jinman Kim

IEEE Journal of Biomedical and Health Informatics

|March 31, 2022

Summary

This summary is machine-generated.

A new interpretable transformer model, TraP-VQA, enhances pathology visual question answering (PathVQA) by integrating vision and language features. This approach improves answer accuracy and provides visual explanations for medical image analysis.

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Computational pathology

Background:

Pathology visual question answering (PathVQA) holds significant healthcare potential but faces adoption challenges due to limitations in integrating visual and textual data.
Existing PathVQA methods often process image and language features separately, failing to capture essential interactions and lacking interpretability for generated answers.

Purpose of the Study:

To introduce an interpretable vision-language transformer model for PathVQA that effectively integrates image and question features.
To enhance the interpretability of PathVQA systems, allowing for justification of retrieved answers.

Main Methods:

Developed an interpretable transformer-based Path-VQA (TraP-VQA) model.
Embedded transformer encoder layers with vision features from pre-trained CNNs and language features from a domain-specific language model.
Integrated a decoder layer to upsample encoded features for final PathVQA prediction.

Main Results:

TraP-VQA outperformed state-of-the-art methods on a public PathVQA dataset.
Model robustness was validated on an additional medical VQA dataset.
Ablation studies confirmed the effectiveness of the integrated transformer-based vision-language model.

Conclusions:

The proposed TraP-VQA model offers improved performance and interpretability in pathology visual question answering.
The integrated approach successfully captures complex vision-language interactions for accurate medical image analysis.
Visualization results demonstrate the model's ability to explain its reasoning for retrieved answers.