Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images

Abstract

Key Aspects

• 🧠 Pigeon as a Visual Model: The paper introduces the novel concept of using pigeons (Columba livia) as an animal model to study the complex visual skills required in medical diagnostics. This approach is justified by the similarities between avian and human visual systems. The research aims to explore whether pigeons, through operant conditioning with food rewards, can learn to perform tasks typically done by highly trained pathologists and radiologists, thereby providing insights into the perceptual processes involved.
• 🔑 Success in Histopathology Classification: A primary finding is that pigeons demonstrated a remarkable ability to distinguish between benign and malignant human breast histopathology images. After training, they not only achieved high accuracy but were also able to generalize their learning to novel, previously unseen images. This success indicates that the pigeons were not merely memorizing images but were identifying and using key visual features indicative of malignancy, similar to human experts.
• ⚖️ Mixed Performance in Radiology Tasks: The study reveals a critical distinction in the pigeons' abilities when applied to different radiology tasks. While they were successful at detecting microcalcifications on mammograms, they failed at a more complex task: classifying suspicious mammographic masses. In the latter case, the birds could only memorize the training images and could not generalize their knowledge to new examples. This contrast between success and failure highlights the specific limits of their perceptual learning and mimics the varying difficulty of these tasks for human radiologists.
• 🛠️ Implications for Medical Imaging: The abstract concludes by outlining the potential applications of this research. The successes and failures of the pigeon model offer a unique tool for understanding the fundamentals of human medical image perception. Furthermore, these avian observers could serve as a cost-effective and consistent method for assessing the performance of new medical imaging hardware, evaluating the effects of image processing techniques like compression, and aiding in the development of better image analysis tools.

Strengths

• ✅ Comprehensive and clear summary

  The abstract effectively condenses a multi-experiment study into a clear, single paragraph. It successfully outlines the research problem, the novel approach, the key findings across different tasks (histopathology, radiology), and the broader implications, providing a comprehensive yet accessible overview.

  "We report here that pigeons (Columba livia)...can serve as promising surrogate observers of medical images...The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology...proved to be similarly capable of detecting cancer-relevant microcalcifications...the pigeons proved to be capable only of image memorization and were unable to successfully generalize..." (Page 1)
• ✅ Explicitly states novelty

  The abstract clearly states the novelty of the research by highlighting that the use of pigeons for this specific task is a new contribution to the field, immediately establishing the paper's significance.

  "We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented." (Page 1)
• ✅ Balanced reporting of successes and failures

  The abstract provides a balanced account by reporting not only the pigeons' successes (histopathology classification, microcalcification detection) but also their failures (inability to generalize on mammographic masses). This transparency enhances the study's scientific credibility and provides a more nuanced understanding of the model's capabilities and limitations.

  "However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples." (Page 1)

Suggestions for Improvement

• 💡 Explicitly connect findings to computational model development

  This is a high-impact suggestion. The abstract concludes by mentioning the utility for developing "image analysis tools." This could be significantly strengthened by explicitly connecting the pigeon model to the validation and development of computational models, such as machine learning or AI algorithms. Drawing a direct parallel between the pigeons' successes and failures and the challenges faced by AI in medical imaging would frame the research as highly relevant to the current push for automated diagnostics, thereby broadening its appeal and impact.

  "The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools." (Page 1)

  Implementation: Revise the final sentence to more directly state this connection. For instance, modify "...and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools" to something like: "...and may also prove useful in the development and validation of medical imaging hardware and computational image analysis tools, providing a biological benchmark for machine learning algorithms."

Introduction

Key Aspects

• 🗺️ The Medical Imaging Challenge: The introduction establishes the central problem: medical image interpretation is a demanding perceptual skill, and validating new imaging technologies requires trained observers, a process that is costly and time-consuming. It also notes that existing computer-aided substitutes often fail to replicate human performance. This context creates a clear need for an alternative, cost-effective, and reliable method for studying and validating medical image perception, which the paper proposes to address.
• 📚 Justifying the Avian Model: To justify the novel use of pigeons, the authors provide a concise literature review of the birds' remarkable visual capabilities. Citing over 50 years of research, they highlight pigeons' proven ability to discriminate complex stimuli, from basic categories to artistic styles, and their impressive visual memory. Crucially, the text establishes a biological parallel by noting that the underlying neural pathways for visual learning in pigeons are functionally equivalent to those in humans, providing a strong scientific foundation for the study's premise.
• 🎯 Guiding Research Questions: The study's objectives are framed as four explicit research questions that create a logical investigative pathway. The questions progress from establishing basic trainability (Can pigeons learn the task without verbal instruction?) to assessing higher-order cognition (Can they generalize beyond memorization?). The inquiry then probes the model's limits (How do they perform on tasks difficult for humans?) and finally considers real-world value (Could these skills have practical utility?). This structure provides a clear and comprehensive roadmap for the reader.
• 🔬 Experimental Foreshadowing: The introduction concludes by briefly outlining the series of experiments designed to answer the research questions. It foreshadows the use of operant conditioning with food reinforcement to train pigeons on several distinct medical imaging tasks of increasing difficulty. These include classifying breast pathology slides at various magnifications, detecting microcalcifications in mammograms, and the highly challenging task of classifying mammographic masses, setting the stage for the methods and results to follow.

Strengths

• ✅ Clear problem framing and motivation

  The introduction effectively establishes the real-world problem in medical imaging—the perceptual challenges, expense, and time-consuming nature of human expertise and validation—thereby creating a strong and clear motivation for the novel approach proposed.

  "However, such innovations in medical imaging must be validated—using trained observers—in order to monitor quality and reliability. This process, while necessary, can be difficult, time-consuming and expensive." (Page 2)
• ✅ Strong justification for the animal model

  The authors provide a compelling, evidence-based rationale for using pigeons as a model. By citing extensive prior research on their visual acuity, memory, generalization, and—crucially—the functional equivalence of their neural pathways to humans, the introduction proactively addresses potential skepticism about this unconventional choice.

  "Importantly, however, the anatomical (neural) pathways that are involved, including basal ganglia and pallial-striatal (cortical-striatal in mammals) synapses, appear to be functionally equivalent to those in humans [11]." (Page 2)
• ✅ Explicit and logical research questions

  The inclusion of four explicitly stated research questions provides an exceptionally clear roadmap for the reader. This structure methodically outlines the study's progression from basic trainability to generalization, performance limits, and practical utility, setting clear expectations for the paper's scope and findings.

  "In these initial studies, we sought answers to four questions. First, could discrimination... be taught to pigeons...? Second, could pigeons go beyond mere memorization...? Third, how would pigeons perform...? And fourth, if the birds were successful, then could such skills have any practical utility?" (Page 2)

Suggestions for Improvement

• 💡 Explicitly frame pigeons as a benchmark for AI models

  This is a high-impact suggestion. The introduction mentions that automated substitutes can fail to reflect human performance. It could be significantly strengthened by explicitly positioning the pigeon model not just as an alternative to human observers, but as a biological benchmark for developing and validating these increasingly prevalent computational tools (e.g., AI/machine learning). This reframing would immediately connect the research to a major contemporary challenge in medical technology, enhancing its perceived relevance and impact from the outset.

  "Automated computer-aided substitutes are available, but may fail to faithfully reflect human performance in many cases [4–6]. We describe here a potential alternative approach." (Page 2)

  Implementation: In the first paragraph where computer-aided substitutes are mentioned, add a sentence that bridges the gap. For example, after "...may fail to faithfully reflect human performance in many cases [4–6]", consider adding: "A robust animal model could therefore provide a crucial biological benchmark for training and validating the next generation of these computational systems."

Materials and Methods

Key Aspects

• ⚙️ Operant Conditioning Framework: The study is built upon a classic operant conditioning framework. Pigeons, maintained at a controlled weight to ensure motivation, were trained in custom-built chambers that isolated them from external distractions. These chambers featured a touchscreen for presenting images and recording peck responses, and a food dispenser for providing reinforcement. This setup operationalizes the principles of stimulus-response-reinforcement, allowing the researchers to precisely control the visual tasks and use food rewards to systematically shape complex discrimination behaviors, forming the procedural backbone for all experiments.
• 🔬 Histopathology Discrimination Protocol (Exp. 1): Experiment 1 established the core protocol for testing visual discrimination using breast histopathology images. Pigeons were trained to classify benign versus malignant tissue samples at various magnifications. To rigorously test for generalization beyond rote memorization, the design incorporated several key controls: separate, counterbalanced image sets for training and testing; the introduction of rotated and flipped stimuli to promote flexible recognition; and a shift from differential reinforcement (feedback provided) during training to nondifferential reinforcement (no feedback) during testing with novel images.
• ⚕️ Radiology Task Adaptation (Exp. 2 & 3): Experiments 2 and 3 extended the methodology to the domain of radiology to probe the capabilities and limitations of the avian model. The protocol was adapted for two distinct tasks: detecting microcalcifications (small, high-contrast features) and classifying mammogram masses (a more subtle, texture-based task). By using stimuli that were pre-rated for human difficulty and applying the same training and testing logic as in the first experiment, the researchers could directly compare pigeon performance on tasks of varying complexity, effectively mapping the boundaries of their perceptual expertise.
• ⚖️ Stimulus Property Manipulation: A crucial aspect of the methodology was the systematic manipulation of image properties to deconstruct the pigeons' perceptual process. After establishing a baseline, the researchers introduced modified stimuli, including monochrome images with normalized hue and brightness to remove color cues, and images with varying levels of JPEG compression to introduce artifacts. By measuring performance changes in response to these alterations, the study could infer which visual features—such as texture, shape, or color—were most critical for the pigeons' classification accuracy, demonstrating the model's utility for evaluating technical image quality.

Strengths

• ✅ Highly detailed and reproducible training regimen

  The paper provides an exceptionally clear and detailed description of the operant conditioning protocol. It specifies the trial structure, the observing response requirement, the differential reinforcement schedule for training versus the nondifferential schedule for testing, and the use of correction trials. This high level of detail ensures the experimental procedure is transparent and allows for accurate replication by other researchers.

  "If the choice response was correct, then food reinforcement was delivered... If the choice response was incorrect, then food was not delivered and a correction trial with the same exemplar was given... On testing trials, any choice response was reinforced (nondifferential reinforcement)..." (Page 5)
• ✅ Robust design to distinguish learning from memorization

  The methodology includes a robust design to differentiate true conceptual learning from rote memorization. By training pigeons on one set of images (e.g., Set A) and testing them on a completely novel set (Set B) without corrective feedback, the study rigorously assesses generalization. This counterbalanced design is a classic and powerful method to validate that the subjects have learned to identify underlying visual features rather than simply memorizing specific stimulus-response pairs.

  "These novel exemplars consisted of Set B if pigeons had been trained with Set A, and Set A if pigeons had been trained with Set B. Birds were exposed to these novel tissue exemplars only during testing trials and they saw each testing exemplar only once daily." (Page 6)
• ✅ Systematic variation of stimulus parameters

  The study's methodology is strengthened by the systematic manipulation of key stimulus properties, including image magnification, color, luminance, and compression. This approach moves the research beyond a simple demonstration of ability to a more mechanistic investigation of the visual cues the pigeons use. This makes the animal model particularly valuable for assessing the perceptual impact of technical parameters in medical imaging systems.

  "Therefore, we sought to eliminate color and luminance cues to limit the range of features available for discrimination, and also studied the effects of different levels of image compression on accuracy..." (Page 6)

Suggestions for Improvement

• 💡 Quantify manual image adjustments for reproducibility

  This is a high-impact suggestion that directly affects the scientific reproducibility of the stimuli. The methods state that image brightness and contrast were 'manually adjusted' or 'modestly adjusted by hand'. This introduces subjectivity and prevents other researchers from creating perceptually identical stimulus sets. Quantifying the target parameters for these adjustments is essential for rigorous replication, especially for the experiments comparing full-color to monochrome images and those equating sets for human difficulty.

  "After re-coloring, the overall brightness and contrast levels were manually adjusted to minimize differences between cancer and normal samples." (Page 6)

  Implementation: Revise the descriptions of manual adjustments to include objective, quantitative criteria. For example, instead of stating levels were 'manually adjusted to minimize differences,' specify the target parameters, such as: 'Images were adjusted using GIMP's Levels tool to achieve a mean pixel intensity of 128 and a standard deviation of 45 across all images in each set.'

• 💡 Specify software versions and key hardware details

  This is a medium-impact suggestion that aligns with best practices for computational reproducibility. The paper lists several software packages (MatLab, Psychtoolbox, GIMP, Caesium) and hardware components but omits specific version numbers and model details. Software algorithms, particularly for image processing and compression, can change between versions, and hardware like monitors have different color gamuts and luminance capabilities. Specifying these details would eliminate potential confounds and allow for more precise replication of the experimental conditions.

  "Experimental sessions were controlled by a program created and run using MatLab with Psychtoolbox-3 extensions (http://psychtoolbox.org/) [25, 26]." (Page 3)

  Implementation: In the apparatus and stimuli sections, add version numbers for all software used (e.g., 'MatLab R2012b', 'Psychtoolbox-3 v3.0.11', 'GIMP v2.8'). For critical hardware, provide the specific model number of the LCD monitor or at least its key display characteristics (e.g., native resolution, color space coverage like sRGB, and maximum luminance).

Non-Text Elements

Fig 1. The pigeons' training environment. The operant conditioning chamber was...

Full Caption

Fig 1. The pigeons' training environment. The operant conditioning chamber was equipped with a food pellet dispenser, and a touch-sensitive screen upon which the medical image (center) and choice buttons (blue and yellow rectangles) were presented.

Figure/Table Image (Page 4)

First Reference in Text

The chambers (shown in Fig 1) measured 36 cm × 36 cm x 41 cm and were located in a dark room with continuous white noise played during sessions.

Description

• Image Content and Organization: The figure presents a grid of 18 histopathology images, which are microscopic views of tissue samples. These are specifically from human breast tissue specimens that have been categorized as either 'benign' (non-cancerous) on the left or 'malignant' (cancerous) on the right.
• Hematoxylin and Eosin (H&E) Staining: All specimens are stained with hematoxylin and eosin (H&E), a standard staining method in pathology. Hematoxylin stains cell nuclei a purplish-blue color, highlighting the cell's control center, while eosin stains other structures like cytoplasm and connective tissue in various shades of pink. This color contrast makes the tissue's architecture and cellular details visible.
• Multiple Magnification Levels: The images are shown at three different levels of magnification, arranged in rows: 4x (low power), 10x (medium power), and 20x (high power). This progression is analogous to zooming in with a camera, moving from a wide overview of the tissue landscape (4x) to a more detailed view of individual cell groups (20x). The caption notes that this sequence matches the order in which pigeons were trained.
• Visual Characteristics of Benign vs. Malignant Tissue: Visually, the benign samples generally show more organized and well-defined structures, such as circular ducts and lobules, with more pink-staining space between them. In contrast, the malignant samples often appear more chaotic and densely packed with dark purple-staining cells, reflecting the uncontrolled cell growth characteristic of cancer. These visual differences form the basis of the discrimination task for the pigeons.

Scientific Validity

• ✅ The figure provides crucial insight into the experimental stimuli.: Displaying examples of the actual visual stimuli is a critical component of a methods section for a visual perception study. This figure allows the reader to directly assess the nature and potential difficulty of the discrimination task, which is essential for interpreting the study's results.
• ✅ The use of varying magnifications represents a robust experimental design.: The experimental design of training pigeons across multiple magnifications (4x, 10x, 20x) is a methodological strength. It tests whether the animals can learn to identify pathological features at different spatial scales, which mirrors a key skill used by human pathologists and adds a layer of complexity and relevance to the study.
• 💡 The representativeness of the selected examples is not defined.: The text states these are 'representative' images. However, without information on the selection criteria, there is a potential for selection bias. Were these images chosen because they are particularly clear-cut examples, or do they reflect the average difficulty of the entire stimulus set? Acknowledging the difficulty level of these specific examples would strengthen the transparency of the methods.
• 💡 The images lack scale bars for absolute size reference.: For scientific rigor in publishing microscopy images, a scale bar is standard practice. While magnification levels are provided, they are relative and can be affected by display size. A scale bar (e.g., 100 µm) would provide an absolute, objective measure of size within each image, which is more informative and aids in reproducibility.

Communication

• ✅ The figure's grid layout is highly effective for comparison.: The grid layout is exceptionally clear and well-organized. By arranging the images by condition (Benign vs. Malignant) in columns and by magnification in rows, the figure allows for easy and intuitive visual comparison between the categories at each level of detail.
• ✅ The caption is highly informative and enhances the figure's self-sufficiency.: The caption is comprehensive and makes the figure largely self-contained. It clearly identifies the tissue type, staining method, image categories, and the training sequence corresponding to the different magnifications shown. This allows readers to understand the stimuli and the experimental progression without needing to search the main text.
• ✅ The labeling is clear and effective.: The labels for rows ('4x', '10x', '20x') and columns ('Benign samples', 'Malignant samples') are clear, legible, and appropriately placed, which is crucial for the figure's interpretability.
• 💡 Annotating key diagnostic features would improve clarity for a broader audience.: While the images are illustrative, their educational value could be enhanced for a non-expert audience. The key visual differences that define benign versus malignant tissue (e.g., organized ductal structures vs. disorganized sheets of cells) are subtle. Suggest adding annotations like arrows or outlines to a few key examples to highlight these discriminating features, which would clarify the visual challenge presented to the pigeons.

Fig 2. Examples of benign (left) and malignant (right) breast specimens stained...

Full Caption

Fig 2. Examples of benign (left) and malignant (right) breast specimens stained with hematoxylin and eosin, at different magnifications. Pigeons were initially trained and tested with samples at 4x magnification (top row), and then were subsequently transitioned to samples at 10x magnification (center row) and 20x magnification (bottom row).

Figure/Table Image (Page 5)

First Reference in Text

See Fig 2 for a representative sample of images displayed to the birds.

Description

• Image Content and Organization: The figure presents a grid of 18 histopathology images, which are microscopic views of tissue samples. These are specifically from human breast tissue specimens that have been categorized as either 'benign' (non-cancerous) on the left or 'malignant' (cancerous) on the right.
• Hematoxylin and Eosin (H&E) Staining: All specimens are stained with hematoxylin and eosin (H&E), a standard staining method in pathology. Hematoxylin stains cell nuclei a purplish-blue color, highlighting the cell's control center, while eosin stains other structures like cytoplasm and connective tissue in various shades of pink. This color contrast makes the tissue's architecture and cellular details visible.
• Multiple Magnification Levels: The images are shown at three different levels of magnification, arranged in rows: 4x (low power), 10x (medium power), and 20x (high power). This progression is analogous to zooming in with a camera, moving from a wide overview of the tissue landscape (4x) to a more detailed view of individual cell groups (20x). The caption notes that this sequence matches the order in which pigeons were trained.
• Visual Characteristics of Benign vs. Malignant Tissue: Visually, the benign samples generally show more organized and well-defined structures, such as circular ducts and lobules, with more pink-staining space between them. In contrast, the malignant samples often appear more chaotic and densely packed with dark purple-staining cells, reflecting the uncontrolled cell growth characteristic of cancer. These visual differences form the basis of the discrimination task for the pigeons.

Scientific Validity

• ✅ The figure provides crucial insight into the experimental stimuli.: Displaying examples of the actual visual stimuli is a critical component of a methods section for a visual perception study. This figure allows the reader to directly assess the nature and potential difficulty of the discrimination task, which is essential for interpreting the study's results.
• ✅ The use of varying magnifications represents a robust experimental design.: The experimental design of training pigeons across multiple magnifications (4x, 10x, 20x) is a methodological strength. It tests whether the animals can learn to identify pathological features at different spatial scales, which mirrors a key skill used by human pathologists and adds a layer of complexity and relevance to the study.
• 💡 The representativeness of the selected examples is not defined.: The text states these are 'representative' images. However, without information on the selection criteria, there is a potential for selection bias. Were these images chosen because they are particularly clear-cut examples, or do they reflect the average difficulty of the entire stimulus set? Acknowledging the difficulty level of these specific examples would strengthen the transparency of the methods.
• 💡 The images lack scale bars for absolute size reference.: For scientific rigor in publishing microscopy images, a scale bar is standard practice. While magnification levels are provided, they are relative and can be affected by display size. A scale bar (e.g., 100 µm) would provide an absolute, objective measure of size within each image, which is more informative and aids in reproducibility.

Communication

• ✅ The figure's grid layout is highly effective for comparison.: The grid layout is exceptionally clear and well-organized. By arranging the images by condition (Benign vs. Malignant) in columns and by magnification in rows, the figure allows for easy and intuitive visual comparison between the categories at each level of detail.
• ✅ The caption is highly informative and enhances the figure's self-sufficiency.: The caption is comprehensive and makes the figure largely self-contained. It clearly identifies the tissue type, staining method, image categories, and the training sequence corresponding to the different magnifications shown. This allows readers to understand the stimuli and the experimental progression without needing to search the main text.
• ✅ The labeling is clear and effective.: The labels for rows ('4x', '10x', '20x') and columns ('Benign samples', 'Malignant samples') are clear, legible, and appropriately placed, which is crucial for the figure's interpretability.
• 💡 Annotating key diagnostic features would improve clarity for a broader audience.: While the images are illustrative, their educational value could be enhanced for a non-expert audience. The key visual differences that define benign versus malignant tissue (e.g., organized ductal structures vs. disorganized sheets of cells) are subtle. Suggest adding annotations like arrows or outlines to a few key examples to highlight these discriminating features, which would clarify the visual challenge presented to the pigeons.

Fig 3. Monochrome images with equated hue and brightness, at different levels...

Full Caption

Fig 3. Monochrome images with equated hue and brightness, at different levels of compression. The original images at 10x magnification were converted to grayscale, colored with a single hue, and had their overall brightness and contrast equalized as closely as possible.

Figure/Table Image (Page 7)

First Reference in Text

Monochrome stimuli. The 10x stimuli at 0° were used, but were converted to monochrome and equated in hue and brightness to eliminate those image properties as variables (see Fig 3, top row, for representative images).

Description

• Monochrome and Equalized Images: This figure displays a grid of histopathology images that have been digitally manipulated to test which visual cues pigeons use for classification. The original 10x magnification color images were first converted to monochrome (single color) by making them grayscale and then applying a uniform purplish hue. This process, known as pseudocoloring, removes color differences as a variable. The caption states that brightness and contrast were also adjusted to be as similar as possible across all images.
• Levels of Image Compression: The figure's main purpose is to show the effects of image compression, a method for reducing a digital file's size, which can degrade image quality. The rows represent three different levels of this compression. The top row, labeled '1:1', shows the baseline uncompressed images. The middle row ('15:1') and bottom row ('27:1') show the same images after being compressed to be 15 and 27 times smaller, respectively. This compression introduces visible distortions, known as artifacts, such as blockiness and a loss of fine detail, which are more severe in the bottom row.
• Benign vs. Malignant Comparison: Similar to the previous figure, the images are separated into columns of 'Benign samples' (non-cancerous) and 'Malignant samples' (cancerous). This layout allows for a side-by-side comparison to see how the features that distinguish these two conditions are affected by the removal of color cues and the introduction of compression artifacts.

Scientific Validity

• ✅ The image manipulation represents a robust experimental control.: The systematic removal of color and normalization of brightness/contrast is a strong experimental control. This manipulation allows the researchers to isolate the importance of morphological and textural information for the discrimination task, providing a more rigorous test of what the pigeons are actually learning.
• ✅ The investigation of compression artifacts adds practical relevance to the study.: Testing the effect of image compression is highly relevant to the field of digital pathology, where managing large file sizes is a practical challenge. By assessing how performance changes with compressed images, the study explores the practical utility of using pigeons as 'surrogate observers' for tasks involving real-world image quality issues.
• 💡 The subjective description of image equalization lacks quantitative support.: The caption describes the brightness and contrast equalization as being done 'as closely as possible,' which is a subjective statement. For greater methodological rigor, the authors should provide quantitative data (e.g., mean luminance, pixel intensity standard deviation) for the benign and malignant image sets to objectively demonstrate how successful the equalization process was.
• 💡 The images are missing standard scale bars for absolute size reference.: As with previous figures, these microscopy images lack scale bars. While the 10x magnification is stated, an absolute scale bar (e.g., in micrometers) is the standard for scientific publication. It would provide an objective measure of the size of cellular structures and help in assessing the impact of compression on features of a specific size.

Communication

• ✅ The figure's organization effectively communicates the experimental variables.: The grid layout is highly effective. By organizing images by diagnosis (columns) and compression level (rows), the figure allows for an intuitive and direct comparison of how compression artifacts affect the visibility of features in both benign and malignant tissues.
• ✅ The visualization of compression artifacts is clear and impactful.: The figure successfully visualizes the abstract concept of image compression. The progressive degradation of image quality from the top row (uncompressed) to the bottom row (heavily compressed) is immediately obvious, clearly illustrating the visual challenge being tested.
• ✅ The labeling is clear and effective.: The labels for the rows ('1:1', '15:1', '27:1') and columns ('Benign samples', 'Malignant samples') are clear and well-placed. The caption provides the necessary context to understand these labels.
• 💡 The process of 'equalization' could be more quantitatively described or visualized.: The caption states that brightness and contrast were 'equalized as closely as possible'. This is a subjective description. To improve clarity and rigor, it would be beneficial to add a supplementary figure or data showing the luminance histograms for the benign and malignant image sets to quantitatively demonstrate the degree of equalization achieved.