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Vision01:24

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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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Language01:16

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Language and Cognition01:27

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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IPATH: A Large-Scale Pathology Image-Text Dataset from Instagram for Vision-Language Model Training.

S Mirhosseini1, T Rai2,3, P Diaz-Santana4

  • 1Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK. contact@erfan.uk.

Journal of Imaging Informatics in Medicine
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

Researchers created the IPATH dataset from Instagram images to train AI for pathology. This AI model, IP-CLIP, shows strong diagnostic accuracy, improving medical image analysis.

Keywords:
CLIPDigital pathologyFoundation modelsImage–text datasetsInstagramMultimodal AISocial mediaZero shot classification

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Area of Science:

  • Artificial Intelligence
  • Computational Pathology
  • Medical Informatics

Background:

  • Artificial intelligence (AI) can detect subtle patterns in pathology images, enhancing diagnostic accuracy.
  • A significant barrier to AI development in pathology is the scarcity of large, publicly available, annotated image datasets.
  • Social media platforms offer a potential, yet unexplored, source for medical image data.

Purpose of the Study:

  • To curate a novel dataset of pathology images from Instagram for AI model training.
  • To develop and evaluate a multimodal AI model (IP-CLIP) using this curated dataset.
  • To demonstrate the utility of social media data in advancing AI for medical image analysis.

Main Methods:

  • Curated the IPATH dataset, comprising 45,609 pathology image-text pairs from Instagram, using automated classifiers, large language models, and manual filtering for quality control.
  • Developed IP-CLIP by fine-tuning a pre-trained CLIP model on the IPATH dataset.
  • Evaluated IP-CLIP's performance on seven external histopathology datasets using zero-shot classification and linear probing, and assessed image-text alignment via retrieval on a held-out subset.

Main Results:

  • IP-CLIP consistently outperformed the original CLIP model on external datasets in zero-shot classification and linear probing tasks.
  • IP-CLIP achieved performance comparable to or exceeding state-of-the-art pathology vision-language models, despite training on a smaller dataset.
  • IP-CLIP demonstrated superior image-text alignment compared to CLIP and specialized models in retrieval tasks.

Conclusions:

  • The IPATH dataset, sourced from Instagram, is a valuable resource for developing AI in medical image classification.
  • Leveraging social media data can effectively address the scarcity of annotated medical images for AI training.
  • The developed IP-CLIP model shows significant potential for enhancing diagnostic accuracy and decision support in pathology.