CatScreen: A Large MultiModal Benchmark Dataset for Cataract Screening
Authors: Mahapara Khurshid
Sonam Kumar
Dr Anusuya Bhattacharyya
Dhruve Kiyawat
Anshul Chauhan
Suklengmung Buragohain
Harsha Bhattacharjee
Limalemla Jamir
Vishali Gupta
Mona Duggal
Mayank Vatsa
Richa Singh
Venue: Accepted by TMLR ()

Abstract: Low-cost slit-lamp imaging holds significant potential for transforming eye care by facilitating affordable and scalable cataract diagnosis. However, the development of robust, generalizable AI-based cataract screening solutions is currently constrained by the limited availability of large-scale, richly annotated datasets. To address this critical gap, we introduce CatScreen, a comprehensive multimodal benchmark dataset specifically designed for cataract screening, comprising approximately 18,000 slit-lamp images collected from 2,251 subjects using a portable slit-lamp camera. CatScreen is structured into three subsets: (i) a clean set meticulously annotated using a structured multi-tier framework involving trained optometrists with final validation by an experienced ophthalmologist across clinically relevant dimensions, including image gradability, quality assessment, illumination type, diagnostic classification, cataract subtype, and severity grading according to established standards; (ii) a noisy-labeled set that simulates real-world annotation inaccuracies; and (iii) an unlabeled set intended to foster the development of self-supervised and semi-supervised learning approaches. Furthermore, CatScreen integrates extensive subject-level metadata encompassing demographics, lifestyle factors, and detailed clinical histories, and includes a subset with anatomical and pathological annotations to support multimodal modeling and anatomically grounded analysis. We present baseline experiments under independent, structured sequential, and multitask prediction settings in both unimodal and multimodal configurations. These results establish initial benchmarks for CatScreen and demonstrate the value of metadata for selected diagnostic tasks, while also highlighting open challenges, such as class imbalance and fine-grained subtype discrimination. CatScreen is intended as a benchmark resource for future research in cataract screening, robust learning, semi-supervised learning, and interpretability-oriented analysis.  The database is available at:  https://iab-rubric.org/resources/healthcare-datasets/catscreen.

Mona Duggal Published in Transactions on Machine Learning Research (03/2026)
CatScreen: A Large MultiModal Benchmark Dataset for Cataract Screening
Mahapara Khurshid
mahapora. 10itj.. oc.in
Indian Institute of Technology Jodhpur, India
Sonam Kumar
conammkamar..kk55@@maa...
Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
Anusuya Bhattacharyya
All India Institute of Medical Sciences (AIIMS), Guwahati, India
Dhruve Kiyawat
darueekxgouut7778@@maad.coo
Indian Institute of Technology Jodksuur, India
Anshul Chauhan
anchsunny&8@@ymail.com
Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
Suklengmung Buragohain
suklen. Baaee.bbooainn@gmii.coom
Sri Sankaradeva Nethrolaya (SSDN), Gwwahati, India
Harsha Bhattacharjee
ssnghy1 @gmail com
Sri Sankaradevo. Nethrolayo (SSDN), Guwahati, India
Limalemla Jamir
bimajonir@@adissguuaaattti
All India Institute of Medical Sciences (AIMSS), Gwwahati, India
Vishali Gupta
mishalisara@yaahoocoo.
Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
monaduggal2@gmaci.com
Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
Mayank Vatsa
meatsa@kitj..cm
Indian Institute of Technology Jodhpur, India
Richa Singh
richa oc. in
Indian Institute of Technology Jodlpur, India
Reviewed on OpenReview: https: openreview. net/ forum? id= cF
Abstract
Low-cost slit-lamp imaging holds significant potential for transforming eye care by facil- itating affordable and scalable cataract diagnosis. However, the development of robust, generalizable Al-based cataract screening solutions is currently constrained by the limited availability of large-scale, richly annotated datascts. To address this critical gap, we intro- duce CatScreen, a comprehensive multimodal benchmark dataset. specifically designed for cataract screeming, comprising approximately 18,000 slit-lamp images collected from 2,251 subjects using a portable slit-lamp camera. CatScreen is structured into three subsets: (i) a clean meticulously annotated using a structured multi-tier framework involving trained optometrists with final validation by experienced ophthalmologist across elinically rele-

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Published in Transactions on Machine Learning Research (03/2026)
(a)
(b)
(c)
Figure 1: Sample images of eyes for (a) normal, (b) cataract condition characterized by lens clouding, and (c) various other eye conditions affecting different ocular structures.
vant dimensions, including image gradability, quality assessment, illumination type, diag- nostic classification, cataract sublype, and severity grading according to established stan- dards; (ii) a noisy-labeled set that simulates real-world annotation inaccuracies; and (iii) an unlabeled set intended to foster the development of self-supervised and semi-supervised learning approaches. Furthermore, CatScreen integrates extensive subject-level metadata encompassing demographics, lifestyle factors, and detailed clinical histories, and inchudes subset with anatomical and pathological annotations to support multimodal modeling and anatomically grounded analysis. We present baseline experiments under independent, structured sequential, and multitask prediction settings in both unimodal and multimodal configurations. These results establish initial benchmarks for CatSereen and demonstrate the value of meladala for selected diagnostic lasks, while also highlighting open challenges, such as class imbalance and fine- greeggaiied subtype discrimination. CatScreen is intended as benchmark resource for future rescarch cataract sereening, robust learning, semi- supervised learning, and interpretabllity-oiittte analysis. The database available at: https://iab-ruurice htt.://///ssoorrss//eaaaeeeeeeee catssreee.....
1 Introduction
Cataract is characterized by clouding of the eye's natural lens, resulting in diminished visual acuity. If left untreated, cataracts can progress to complete blindness or significantly impair an individual's mental well-being Wang et al. (024b)). Figure 1 illustrates representative examples of cataractous lens compared to healthy eyes and natural crystalline lens affected by other ocular conditions. Globally, cataract remains the predominant cause of blindness, impacting approximalely 15.2 million individuals, constituting about 45% of global blindness cases (Steinmetz et al. 2021 of the Global Burden of Disease Study et al. 2024). Although cataracts have traditionally been prevalent among elderly populations, recent cpidcmiological studics report a worrying rise in prevalence among younger age groups. This demographic shift is associated with multiple modifiable and non-modifiable risk factors, including smoking, radiation exposure, and an increasing inci- dence of diabetes (Thompson & Lakhani (2015). Timely detection and early surgical intervention through lens replacement remain the definitive treatment options, thereby emphasizing the necessity of effective and accessible screening methods.
Conventionally, cataract diagnosis relies heavily on manual examinations performed by trained ophthalmolo- gists using slit-lamp microscopy. This clinical evaluation requires significant expertise and extensive practical experience to accurately detect cataracts, classify their subtypes, and grade their severity. Despite its effec- tiveness, this traditional diagnostic approach faces substantial limitations, particularly in resourcc-limited settings characlerized by insufficient qualified healtheare personnel (Flaxman al. 2017). Consequently, significant fraction of the global population, especially those in underdeveloped and underserved regions, lacks access to timely diagnosis and effective treatment options. This disparity underscores the urgent need for innovative, scalable, and accessible diagnostic solutions that can bridge existing healthcare gaps and enhance ocular health outcomes for diverse populations.
Recent advancements in Artificial Intelligence (AI) and Machine Learning (MI.) present promising avenues address these diagnostic challenges. AI techniques have shown substantial potential in medical imaging anal- ysis, particularly in detecting, segmenting, classifying, and grading various ocular diseases (Gour et al. 2023
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Table 1: Existing slit-lamp image- based datasets for cataract diagnosis and their respective characteristics
Device
No. of
No. of
Image Quality?
Discase
Author
Metadata
Remarks
Used
Subjects
Images
Grading?
Topcon DC-1 digital
Foong
2007
1000
1000
Nuclear Calaract
slit-lamp
Infantile Cataract
Jiang et al.
2021
slit-lamp
536
886
X
Single Center
Infantile Cataract
Jiang et al.
2001
slit-lamp
433
757
Multicenter
SL-D7[TMS, Inc]
Cataract detection
Son et al.
2022
596
1972
D850 [Nikon, Inc]
and grading
Wang et al.
22244a
slit-lamp
2150
2150
X
X
V
Cortical cataract
38320
Shimizu al.
2003
Smart Eye Camera
1812
Nuclear cataract
frames
Cataract detection,
Remidio Portable Slit Lamp PSL-D20
grading, quality analysis, multimodal data
Proposed CatScreen
2251
18640
Integrating AI-driven automated systems into cataract scrcening workflows can significantly enhance diag- nostic accuracy, elficiency, and accessibility, offering profound benefits to remote and resource-consl enviromments. However, the development and successful deployment of robust Al-based diagnostic tools fundamentally depend on the availability and quality of comprchensive, diverse, and meticulously annotated datasets.
Several research initiatives have leveraged slit-lamp imaging datasets to automate cataract detection and classification. However, current slit-lamp datasets often suffer from limitations related to data diversity, completeness, and annotation depth, hindering their applicability to generalizable and robust diagnostic systems. For instance, Foong et al. (2007) and Shimizu et al. (2023) focused primarily on muclear cataracts, while Wang et al. (2024a) concentrated exclusively on cortical cataracts. Such narrowly focused datasets inherently lack comprehensive diversity, thus limiting their generalizability. Similarly, datasets provided by Jiang et al. (2021) and [Son al. (2022). although extensive, primarily offer limited annotation dimensions, rostricting the clinical relevance and interprotability of AT solutions derived from them. Notably absent from these datasets are critical parameters such as detailed assessments of image quality, precise classification of cataract subtypes, and standardized grading of cataract severity, all of which are essential elements in comprehensive ophthalmic diagnosis. Consequently, existing Al-based methods developed from such datasets encounter significant biases and constraints in real-world diagnostic scenarios.
To systematically address these limitations and foster the development of robust and clinically relevant Al-based cataract screening tools, we introduce CatScreen, an extensive, multimodal, and comprehensively amnotated dataset acquired through an affordable, portable slit-lamp imaging device. While conventional tabletop slit. lamps offer superior optical stability, their cost, size and in infrastructure requirements limit deployment in outreach and ansource-constrainee settings (Kaushik et al.) 2026). In contrast, smartphone- integrated portable slit-lamp systems enable scalable and accessible cataract screening (Liu et al. 2026). However, these systems introduce increased variability in image quality due to factors such as illumination inconsistency, motion artefacts, and operator dependence (Fung et al.) (024)). CatSereen is designed to reflect community-level screening conditions, including variability in image quality, illumination, and operator dependence, thereby providing a realistic benchmark for studying robustness and generalization. The dataset comprises over 18,000 slit-lamp images collected from approximately 2,251 diverse subjects. Each image is thoroughly annotated across several clinically relevant dimensions, including diagnostic classification, image gradability, quality assessments, calaract subtype identification, and severity grading, all adhering to established ophthalmological standards. Additionally, comprehensive subject-level metadata capturing essential demographic variables, lifestyle factors, and clinical historics accompanics the dataset. In addition, a subset of images includes detailed region annotations such as the lens, cornea, iris, and pathological regions that support anatomically grounded analyses and future interpretabllity-orieeee modelling.
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By providing a richly annotated and multiddmensiooall ocular dataset, CatScreen uniquely addresses critical shortcomings of existing datasets and sets foundational resource for benchmarking multimodal cataract screening models and supporting future work in explainability, robust learning, and clinically motivated analysis. The depth and diversity of annotations ensure that AI methods developed using CatScreen can support development and benchmarking across diverse acquisition settings, enhancing their utility in real- world ophthalmic diagnostics. Given these comprehensive features, CatScreen directly addresses the following critical clinical and technical research questions:
Q1: How slit-lamp image quality be reliably and objectively evaluated?
Q2: How can the type of illumination employed in slit-lamp imaging be accurately identified, and its impact on diagnostic oulcomes quantified?
Q3: How can slit-lamp images be effectively categorized into normal, cataractous, or other ocular conditions?
Q4: How can specific cataract sublypes be accurately identified and differentiated?
Q5: Upon diagnosing cataract presence, how cataract severity be consistently and objectively assessed?
The structured and comprehensive nature of CatSereen ideally positions it for diverse screening and diag- nostic modeling frameworks, including structured sequential analyses and multitask learning methodolggies, as elaborated in the subsequent problem formulation section.
2 Problem Formulation
The problem addressed in this work is the aulomated screening and diagnostic assessment of cataracts, aiming to replicate the detailed multi-stage clinical evaluation typically performed by ophthalmologists. Specifically, this assessment involves evaluating the quality of slit-lamp images, classifying illumination con- ditions, diagnosing the presence or absence of cataracts, identifying specific cataract subtypes, and grading cataract severity. The structured assessment ut ilizeee slit-lamp images paired with comprehensive patient. metadata to provide clinically informed predictions.
Formally, we denote an imput slit-lamp image as E E R'XWxC where H, W, and C correspond to image height, width, and color channels, respectively. Additionally, patient metadata is represented by a feature vector M e Rd, containing demographic details, lifestyle factors, and clinical histories, such as age, gender, diabetic status, and ocular modical records. The task objective is predict a of clinically relevant diagnostic attributes, denoted as A (Q,T, D,C, G), where each attribute represents:
Q E Good, Acceptable, Poor: Image quality assessment.
Diffuse, Direct Focal, Retro: Illumination type classification.
D E Normal, Cataract, Others: Diagnostic categorization.
C' e No Cataract, Nuclear, Cortical, PSC, Pseudophakia, Others: Cataract subtype identification.
G E Not Applicable, Mild, Severe: Severity grading of cataract.
This diagnostic formulation can be approached via two distinct yet complementary methodologies: (1) structured sequential analysis, mirroring the sequential decision-making workflow of clinical practitioners, or (2) multitask learning, where all diagnostic altributes are concurrently predicted from shared inputs.
To reflect the clinically accepted ordering of decisions cataract screening, we formulate a structured sequential dependency model, Structured Sequential Analysis (SSA), over the target attributes. The model is formally defined follows:
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Q fq)),
(1)
T fr(I,Q,er),
(2)
D Ip(I, M, en))
(3)
C fo(1, M, D,ec),
G
where EQ, ED; EG EG denole exogenous noise variables, thus providing structured representation of lask dependencies that supports more transparent discussion of the diagnostic workflow.
Alternatively, the multitask learning formulation employs unified predictive model fo parameterized by model weights 0, simultaneously predicting all attributes:
- fo(,
(6)
This model is optimized by minimizing weighted sum of task-specific classification losses:
((0) = d Xrlr ADCD + Aclo + AoCa,
(7)
with each individual loss C. defined by categorical cross-entropy:
C(y.0) Σ yi log(ús); ==1
(8)
where y is the one-hot encoded ground-truth vector, and ğ is the predicted probability distribution over K classes.
Acknowledging the advantages inherent in each modeling approach, explicitly structured our CatScreen dataset to facilitate both methodologies. This flexibility ensures the development of robust, versatile, and clinically meaningful autorated cataract screening systems. This sequential formulation draws on established clinical workflows to provide a structured prediction framework suited for benchmarking, without imposing formal causal assumptions on the relationships among diagnostic attributes.
3 CatScreen Dataset
The CatScreen dataset is a meticulously curated resource, comprising over 18,000 slit-lamp images acquired using low-cost, portable handheld slit-lamp cameras. Aiming to deliver a comprehensive and affordable solution for cataract diagnosis, this dataset addresses critical gaps identified in existing ophthalmic image repositories. Prioritizing patient privacy and adhering strictly to ethical guidelines, all Protected Health Information (PHI), including names, guardian details, contact numbers, email addresses, and other sensitive data, has been carefully anonymized and removed. To maintain data integrity without compromising con- fidentiality, each image is assigned a unique, non-identifying identifier indicating patient ID, pupil dilation state (dilated or undilated), eye side (right or left), and capturing device details. Preliminary preparations, including rigorous site selection, commenced in August 2022, leading to an extensive data collection phase spanning from December 2022 through January 2024. This section elaborates on the detailed prerequisites, device specifications, data acquisition procedurcs, and the exhaustive annotation mothodologies utilized.
3.1 Pre-requisites and Device Specifications
This subsection describes the essential preparatory steps, patient recruitment protocols, exclusion criteria, and technical specifications of the imaging device employed.
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1. Ethical Considerations: Prior to data collection, formal approvals obtained from the Institutional Ethics Committees of the Postgraduale Institute of Medical Education and Re- search (PGIMER) (approval number (LL//EEC-11/222-206-22 (SSN/
and Sri Sankaradeva Nethralaya Adherence to the ethical standards outlined in the Declaration of Helsinki was strictly maintained throughout the study. Participants were given detailed briefings on the study's objectives, methodologies, and potential implications. Written informed consent was obtained from all participants, and dedicated sessions were held address and resolve the questions of the participants.
2. Exclusion Criteria: To guarantee data consistency, individuals with active ocular infections diagnosed type 1 diabetes mellitus were excluded from participation. These criteria ensured the collection of relevant and high-quality data suitable for developing reliable diagnostic models.
Device Specifications: The imaging data was captured using the Remidio Portable Slit Lamp PSL-D20 (Remidio Innovative Solutions Pvt. Ltd., Bangalore, India), handheld, versatile, and portable digital slit-lamp camera. Designed explicitly for elficient mobile eye examinations, the PSL-D20 device offers adjustable slit widths ranging from to 12mm and slit angles adjustable within +45 degrees, supporting both diffuse and direct focal illumination. The device scamlessly integrates with an Apple iPhone SE camera, leveraging its high-resolution imaging capabilities. It supports multiple specialized imaging modes, including standard color imaging, green (red-free), and black illumination. Equipped with maguification options of 10x and 16x, and a working distance of 61mm, the PSL-D20 ensures high-quality imaging under diverse clinical scttings. Images are captured high resolution of 3024x 4032 pixels, while videos are recorded at 1920x 1080 pixels, further enhancing data clarity and clinical applicability.
3.2 Data Collection
The CatScreen dataset comprises 18,640 slit-lamp images from 2,251 diverse participants collected across multiple clinical sites. In addition to visual imagery, CatScreen includes comprehensive metadata captur- ing detailed socio-demographic and clinical characteristics of each participant, significantly enriching the datasct's analytical potential. To facilitate various research approaches, CatScreen is systematically catego- rized into three distinct subsets:
Featuring 9,915 meticulously annotated images from 1,271 participants, this subset serves as the foundation for robust, supervised learning model training. Domain experts provided exhaustive annotations covering essential parameters including image gradability, diagnostic cate- gorization, cataract subtype identification, severity grading, and illumination type. The detailed distribution of annotations across patients and attributes is presented in Table 2a
Clean Set:
Consisting of 3,267 images from 505 participants, this subset includes annotations with varying degrees of accuracy to emulate realistic clinical environments, where annotations may inherently contain errors. This set annotated solely at Level without, subsequent review by annotators Levels and 3. Since level 1 annotations may contain errors due differences in expertise and diagnostic precision, this subset inherently incorporates real-world label noise, particularly representative of community-basee based screening contexts where specialist review is often unavailable. Such a datasct aids in the development and evaluation of models resilient to annotation noise.
Noisy Set:
Unlabelled Set: Containing 5,481 images without diagnostic annotations, this subset facilitates the exploration of innovative approaches such as sclf-supervisod, semi-supervised, and active lcarning methodologies, allowing effective utilization of unlabeled data augment model robustness and generalizability.

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3.2.1 Image Collection Protocol
Images were systematically captured following the protocols recommended by the WHO simplified grading system. These images were collected under both dilated and undilated pupil conditions, using the following specialized imaging techniques:
Direct Optical Section Images: Captured at 10x magnification, with a slit width of 0.2 mm and & slit-beam orientation of 30 degrees, enabling detailed visualization of distinct lens layers.
Direct Diffuse Illumination Images: Obtained at 5x magnification, with the illuminating arm positioned at a 45-degree angle relative lo the microscope. This approach provides comprehensive visualizations of the anterior segment of the eye in both dilated and undilated states.
Indirect Retro--llumination Images: Acquired under strictly controlled, dimly lit conditions (below 15 lux) with slit cump-mounted iPhone SE camera flash, exclusively in dilated conditions, offering enhanced visualization of posterior lens structures and subtle abnormalities.
3.2.2 Comprehensive Metadata
To maximize the clinical and rescarch value of the CatSereen datasct, an extensive array of patient metadata was collected, encompassing broad spectrum of factors potentially influencing cataract development and progression. Summarized comprehensively in Table 25 key metadata elements include:
Demographic Data: Participant ages ranged from 35 to 92 years, averaging approximately 57
years, with well-balanced gender distribution comprising 45% females and 54% males.
Hcalth Conditions: Clinical historics documenting the presence of diabetes, hypertension, and other systemic conditions that could influence ocular health.
Eye Hcalth History: Detailed ocular health records capturing previous incidents of eye inflam- mation, myopia, ocular surgeries, eye trauma, and topical ocular disorders.
Through its careful curation, rigorous annotations, and extensive metadata, CatScreen provides valuable benchmark resource for advancing AT-driven cataract screening research and supporting future clinically relevant studies, particularly for underserved and resource-limited settings.
4 Data Annotation
Ensuring accuracy and consistency in dataset annotation is pivotal for developing robust and clinically reliable diagnostic models. To achieve this objective, the CatSereen dataset employed meticulous three- tiered annotation process, structured to maximize precision, minimize annotation biases, and maintain high- quality standards throughout. Image acquisition and initial annotation were conducted by formally trained optometrists who underwent structured institutional training in slit-lamp operation, ocular imaging, and quality assurance. Imaging consistency maintained through standardized acquisition protocols, on-site supervision, and real-time quality checks prior to annotation.
4.1 Three-tiered Annotation Structure
The annotation procedure was designed leverage progressively specialized expertise each annotation level:
Level 1 Initial Annotation: Two experienced optometrists, each holding a bachelor's degree in optometry with a minimum of three years of hands-on experience retinal and anterior seg- ment imaging, performed initial annotations. These annotations provided foundational layer for subsequent refinement.

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Label
Classes
Train
Val
Test
Total Images
Normal
1845
251
545
2641
Cataract
3627
523
996
5146
Diagnosis
Others
1526
224
378
2128
Not Applicable
1845
251
545
2641
Nuclear Cataract
3357
479
924
4760
Cortical Cataract
184
30
48
262
Cataract Type
Posterior SC
75
14
24
113
Pseudophakia
1495
220
365
2080
Others
42
4
13
59
Not Applicable
3371
475
923
4769
Cataract Grade
Mild
2970
428
796
4194
Severe
657
95
200
952
Good
134
22
176
Image Quality
Acceptable
6112
871
1680
8663
Poor
752
105
219
1076
Diffuse Illumination
2130
296
606
3032
Illmmiaaioon type
Direct Focal
2996
423
795
4214
Retro
1872
279
518
2669
(a)
Set
Gender Clean
Age
Participants
Noisy
Clean
Noisy
Clean Set.
1271
Females Males
578
231
Max
92
86
Noisy Sct
505
693
274
Min
35
51
Mean Std
57.2
9.96
61 + 7.22
Diabetes Status
Hypertension Status
Systemic Illness Status
Clean
Noisy
Clean
Noisy
Clean
Noisy
No
815
199
No
710
181
No
1002
295
Yes
456
306
Yes
561
324
Yes
269
210
Eye Problem Status
Use of Spectacles
Ocular Surgery Done?
Clean
Noisy
Clean
Noisy
Clean
Noisy
No
396
195
No
367
166
No
667
354
Yes 875
310
Yes
904
339
Yes
604
151
(b)
Table 2: Showcasing the (a) distribution of each label in training, validation and testing, and (b) summarizing the distribution of participants of clean and noisy sets based on sociodemographic factors and other clinical characteristics.
Level - Secondary Review and Refinement: senior optometrist possessing a master's degree in optometry and over five years of clinical and imaging experience conducted an exhaustive secondary review. This intermediate stage involved meticulous refinement and correction of initial annotations to resolve ambiguities and discrepancies.
3. Level 3 Expert Validation:
Final validation and oversight were provided by retina-trained ophthalmologist with over decade of clinical and diagnostic experience in slit-lamp- anterior segment evaluation. This expert-level validation ensured maximum accuracy, consistency, and clin- ical relevance of the final annotations, thereby enhancing the dataset's reliability and applicability in developing diagnostic AI solutions.
The annotation process was systematically divided into three essential phases: gradability assessment, ground-truth collection, and anatomical and pathological annotation.
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Quality
Diagnosis
Cataract Grade
Cataract Type
Figure 2: Presents the samples of the CatScreen in diffuse illumination setting across varions labels.
4.2 Gradability Assessment
Image gradability is crucial for ensuring diagnostic accuracy in ophthalmic imaging research. Recognizing its importance, a rigorous two-stage gradability asscssment protocol was developed and implemented.
In the first stage, trained opticians assessed the image quality in real-time at the point of capture. Images failing predefined stringent criteria related to clarity, sharpness, and contrast were immediately discarded and replaced with newly captured images. The second stage involved Level annotators performing a comprehensive reevaluation of the images using identical quality standards. Only those images passing both stages of rigorous scrutiny were deemed gradable and included for detailed annotation and further analysis.
The key parameters evaluated during this gradability assessment included:
Clear visibility of at least 80% of the anterior segment of the eye.
Distinet visibility of critical morphological features including the iris, lens, cornea, and other anterior ocular structures.
Optimal sharpness and contrast for accurate clinical interpretation.
At least 95% clarity of all morphological landmarks.
4.3 Ground-truth Collection
Each selected image underwent detailed multi-label annotation process, capturing comprehensive diagnos- tic insights. Figure 2 showcases the multi-label annotation of some sample images in diffuse illumination.
a. Image Quality: Annotated using a 10-point Likert scale, the image quality ratings were aggregated into three distinct categories mitigate subjective variability: Poor (Grades 1-3), Acceptable (Grades 4-8), and Good (Grades 9-)

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b. Illumination Type: Images were categorized based three illumination techniques commonly used in slit-lamp imaging:
Direct Focal Illumination: Utilizing narrow, focused beam directed to highlight specific anterior ocular structures such as the lens and cornea, enhancing visibility of fine details and layer structures.
Diffuse Illumination: Employing broad, uniform light source, this method provides an overall visualization the anterior segment, suitable for general screening and holistic assessment.
Retro-Illumination Involving light reflected from behind the retina to visualize the posterior lens, this method accentuates subtle abnormalities such as posterior lens opacities.
c. Diagnostic Label: Images were classified into three diagnostic categories:
Normal: Images with no detectable abnormalities.
Cataract: Images exhibiting any evidence of lens clouding.
Others: Images indicating conditions other than cataract, such as pseudophakia, plerygium, con- junctivitis, or corneal scarring. This grouping aggregates all non-cataract cases to align with the primary objective of cataract detection and severity assessment.
d. Cataract Type: Six categorics were utilized for identifying cataract subtypes:
Not Applicable: For cases with no visible opacification.
Nuclear Cataract: Opacities predominantly within the lens nucleus.
Cortical Cataract: Peripheral lens opacities characterized by wedge-like patterns.
Posterior Subcapsular Cataract (PSC): Opacities located posteriorly, affecting the lens cap- sule's back.
Pseudophakia (PP): Presence of artificial intraocular lens.
Others: Any conditions not fitting exclusively into above categories combinations thereof.
Cataract Grade: Cataract severity classified into three distinct grades:
Not Applicable: For normal other non-cataract cases.
Mild: Early-stage cataracts with minimal to moderate lens opacity. This corresponds clinical
grades 1, 2, and 3, which are grouped to mitigate inter-observer variability and subjective bias inherent in fine-grained clinical grading.
Advanced cataracts characterized by dense and extensive lens opacification. This corre- sponds to clinical grades 4 and 5.
Severe:
4.4 Anatomical and Pathological Annotations
To further enhance the dataset's interpretability and clinical relevance, subset of images underwent meticulous iogmentation-baaseee annotations. Using the Oxford VGG Image Annotator tool (available [https://www.. http::///wwwwbbots.ooo rww.ts.ooooooooo ox ac htt/:/gg//ottwarr/viaaaa//aa///a demo. html ), annotators delincated key ocular structures such as the cornea, iris, lens, and sclera. Additionally, pathological features such as cataracts, corneal scars, pterygium, subconjunctival hemorrhages, and edema were accurately identified and annotated. These detailed annotations are critical for training AI models capable of anatomically accurate diagnostic predictions and for validating the clinical interpretability of the models. Sample annotated images are presented in Figure 3 and precise coordinates of each annotation systematically documented.
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Pixel Annotation
Anterior Segment landmarks Lens, comea, iris, eyelids, pupils etc.
De-identified Images database
VIA VGG Annotation Project
Import Images VIA VGG HTML
Export cav or .sonn format
Annotation Repository
Image Classification
Ground truth collection Image Quality, Illumination type, Diagnostic label, Cataract type, Cataract Grading
(a)
Anatomical Structure Category
Eyelids
Eyelashes
Calegory
Codlra-Conjuncctiva
Coreea-iris
Pupillary Zone Silt Beam the Iris
Silt Beam
Abnormal morpholkgies structures
Siit Beam the lesion Pterigium
merphollgges
O
asstonicsl structunaa
Other
Conjuctival Hyperemia
Conjuctival Haemorrage
Conjuctival Oedema
Keratitis
Cataract
Other
(b)
Figure 3: Presents (a) the annotation process where the input image is classified corresponding to multiple labels and (b) various annotations for anatomical and pathological regions in slit-lamp image.
4.5 Quality Assurance Protocol
A comprehensive quality assurance (QA) protocol was implemented, involving initial annotations by Level annotators followed by systematic reviews by Level 2 annotators. For initial QA validation, the first 500 annotated images underwent inter-annotator agreement analysis, specifically targeting diagnostic labels. A kappa score threshold of 0.60 was set as the standard for acceptable agreement. Any discrepancies or disagreements were rigorously addressed in structured group discussions involving the expert Level 3 annotator. Following this initial validation phase, continuous monthly QA checks were conducted, reviewing 25% of annotated images over three consceutive months. This rigorous annotation and QA protocol ensured high levels of annotation precision and consistency, reinforcing CatScreen's suitability as reliable and clinically valuable dataset. for developing advanced ophthalmic diagnostic AI solutions.
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4.6 Data Records
The CatScreen dataset is available for research purposes and a sample snapshot of the dataset is available at https:/ https://iab-rubrie /iiaaaaaabii.orggg....... cro.briicorrggsouureeseesssss rrsourcee/////eee httpurreeeeeaathhr--attttee hhalthhaareddatases// catscreen. There is primary folder named CatScreen with the following directory structure:
Clean: This folder includes a subfolder "Clean images" containing clean image data and a metadata file, clean mela, with detailed socio-demographic and medical information for each participant in the clean set. Additionally, three CSV files for training, testing, and validation are provided, containing label information such as image quality, diagnostic label, severity grade, and more.
Noisy: This folder includes a subfolder "Noisy images" containing noisy image data and a metadata file, noisy_meta, with detailed socio-demographic and medical information for each participant. A CSV file, noisy_labed is also included, providing label information for each attribute.
Unlabelled dataset: This folder includes subfolder 'Unlabelled images'.
A CSV file, 'Region annotation', is also included, containing the filenames and coordinate infor- mation of various regions within each image.
5 CatScreen Framework and Experimental Details
This section presents detailed overview of the CatScreen Framework, aligning it with the mathematical problem formulation described in Section 2. It introduces two complementary modeling approaches, Struc- tured Sequential Analysis and Multitask Learning, and provides descriptions of the evaluation protocols, baseline algorithms, and implementation specifies essential for validation of automated cataract screening solutions.
5.1 CatScreen Framework
The CatScreen Framework is designed to address the automated diagnostic tasks delineated mathematically in Section 2, specifically targeting the accurate prediction of clinically relevant attributes A = (Q, D) from slit-lamp images I and patient metadata M. To establish representative benchmark settings for Cat Screen, we consider two paradigms: Structured Sequential Analysis and Multitask Learning.
1. Structured Sequential Analysis: Figure 4a represents the Structured Sequential Analysis framework. This approach closely mimics the clinical reasoning of the doctors by explicitly modelling the depen- dencies among diagnostic attributes. Each stage builds upon preceding predictions, reflecting the typical sequential decision-making of ophthalmmlogisss..
Starting with a slit-lamp image (I), the model first assesses the image quality (Q) and illumination type (T). These are important as they directly affect the subsequent diagnostic predictions. Based on these intermediate assessments, the model then predicts the overall diagnosis (D) by combining with patient metadata. (M), determining whether the eye is normal, has cataract or shows other conditions. If cataract is detected, the model then proceeds to classify the cataract type and its severity grade. By sequentially building each prediction upon prior oulputs, this approach facilitates explicit feature refinement and error control at each stage, aligning well with medical interpretability requirements.
2. Multitask Learning Framework: Figure 4b presents the multitask approach. Unlike the sequential approach, this framework predicts all the diagnostic attributes jointly from the same set of inputs. A shared feature encoder first extracts global representations from the slit-lamp image (I), enriched by patient metadata (M), thereby capturing visual and contextual information. Separate task-specific classification heads are then used to sinultancously predict image quality (Q), illumination type (T), diagnosis (D), cataract subtype (C), and severity grade (G). This parallel formulation allows the model to leverage correlations among tasks while avoiding explicit sequential dependencies, thus
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Stop
Bad
Image Encoder
Quality Assessment
Good
Illumination
Type
Severity
Grade
Metadata Encoder
Metadata
Diagnosis
Normal
Others
Cataract
Type
Stop
Quality
Illumination
Image Encoder
Type
Diagnosis
Feature Fusion
Severity Grade
Metadata Encoder
Cataract Type
Metadata
Classification
Heads
(b)
Figure Presents the two complementary frameworks: (a) structured sequential analysis framework for automated cataract screening and (b) Multitask learning framework where each label is jointly predicted for automated cataract screening.
enabling efficient end-to-end learning. The overall training objective combines individual classifi- cation losses for each task into composite loss function (as described in Equation 7), promoting sharod representation learning while allowing specialization for each attribute.
5.2 Experimental Protocol and Baselines
To evaluate the effectiveness of these frameworks, rigorous experimental protocols and bascline assessments were developed, ensuring reliability, reproducibility, and generalizability.
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5.2.1 Evaluation Protocol
Table 2a presents the distribution of available labels in the clean set. This part of the CatSereen dataset was partitioned into training, validation, and testing subsets following a 70:10:20 split. To maintain data integrity and avoid data leakage, partitioning was performed strictly at the patient level, ensuring all images from single patient reside exclusively within one subset.. Furthermore, balanced representation of diag- nostic categories across all partitions was carefully maintained, reducing biases and ensuring robust model generalization across varied diagnostic scenarios.
5.2.2 Baseline Algorithms
Benchmark experiments were conducted using both Structured Sequential Analysis and Multitask Learn- ing frameworks to establish foundational performance metrics. The baseline evaluations were performed under both unimodal and multimodal settings, thereby demonstrating the contribution and effectiveness of incorporating metadata.
Structured Sequential Analysis Bascline: In this sctup, each diagnostic attribute including image quality (Q), illumination lype (T), overall diagnosis (D), cataraci sublype (C), and severity grade (G) is predicted independently using separate models, without explicitly cascading outputs from one stage to another. Instead of enforcing explicit sequential conditioning, each prediction implicitly captures relevant cues from the image that may be shared across attributes. For example, although severity and subtype are clinically related, no direct sequential dependencies are imposed during training; each model learns to internally extract latent information from the image that implicitly reflects these relationships. This design serves fundamental baseline to understand the inherent capacity of visual representations to support each task in isolation while still allowing the model to leverage potential cross-attribute signals implicitly embedded in the learned features.
To demonstrate the applicability of this framework, we implement a conditional sequential inference pipeline at the evaluation stage, combining independently trained models to mimic the clinical diagnostic workflow. In this sctup, the predictions from carlier attributes are conditionally usod guide subsoquent tasks. This enables structured decision flow without retraining the individual models. It helps to assess both the intrinsic discriminative capacity of visual representations in isolation and the effectiveness of structured sequential inference when independent predictions are composed in clinically meaningful order.
Multitask Learning Baseline: In contrast, the multitask baseline jointly predicts all labels in a single end-to-end model. common feature extractor first learns an overall representation from the slit-lamp image. Baseline experiments are performed in both unimodal and multimodal settings. separate prediction heads work in parallel to estimate image quality (Q), illumination type (T), diagnosis (D), cataract subtype (C), and severity grade (G). The model is trained using a combined loss that balances each task, helping it learn shared information across tasks while allowing each prediction head to focus its specific details. This formulation enables the model to exploit inter-task correlations more explicitly within single unified architecture and to quantify the impact of metadata inclusion, which supports rapid multi-attribute inference in a clinical setting.
For & comprehensive assessment, diverse deep learning architectures were employed, encompassing con- volutional neural networks (CNNs) and transformer-base
models. Specifically, models such as VGG-16 (Simonyan & Zisserman 2015), ResNet18 (He et al. 2016). ResNet34 (He et al., 2016). MobileNet-V2 (San- dler et al. 2018), EfficientNet-B4 (Tan & Le] 2019 DenscNet-121 (Hann et al. 2017). swin transformer (swin base patch4 window 224) (Liu et al.) 2021 and vision transformer (vit. base _ace__laahhl 224) Dosovitskiy et al. 2021), all pretrained on the ImageNet dataset (Deng et al. 2009), were evaluated to benchmark model performance across different architoctural paradigms. These were selected for their proven performance in image classification, balancing efficiency and feature extraction.
5.2.3 Implementation Details
All models were trained for 50 epochs using the AdamW optimizer with an initial learning rate of 0.0001 and weight decay of 1 10-5. Weighted cross-entropy loss was employed to mitigate class imbalances
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prevalent within ophthalmic datasets. Computational experiments were executed using PyTorch on DGX workstation equipped with 256 GB RAM and four Nvidia V100 GPUs (each with 32 GB memory).
Diagnosis
Cataract Type
1.00
Precision Recall
1.00
Precision Recall
0.75
0.75
0.50
0.25
0.00
ResNet18
VG616
ResNet34
MobileNet, V2
Efficientnet-bl
DenseNet1zi
Swin
VIT
ResNet18
VGG18
ResNet34
MobileNer, V2
Effiientnee-ba
DenseNe1221
Swin
VIT
Cataract Grade
Quality
1.00
8 Precision
1.00
Precision Recall
Recall
0.76
0.75
0.50
0.50
0.25
0.26
0.00
0.00
RetNetis
VoG1é
ResNet34
MobileNet_ V2
Efficientnet-bl bd
DemseNet121
Swin
VIT
ResNet18
VGG16
ResNet34
MobileNet, V2
Efficientnet-bà
DenseNet121
Swin
VIT
Illumination Type
1.00
Precision
Recall
0.75
0.50
0.25
0.00
ResNerl8
VaG16
ResNet34
MobileNet_ 12
Etfiiintnee-bb
DenseNer121
Swin
VIT
Figure 5: Showcasing the mean precision and recall scores across different diagnostic tasks in a unimodal setting. The plots demonstrate how model performance varies with task complexity, highlighting good performance for simpler and balanced classes such illumination and comparatively lower precision-recall trade-offs for fine-grained prediction tasks.
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6 Results and Analysis
This section analyzes the baseline performance outcomes obtained using the Independent Label Prediction, Structured Sequential Analysis and Multitask Learning frameworks. The performance metrics used to eval- uate these approaches include the Fl score, precision, and recall, providing an insight into each framework's 's strengths and limitations. In addition quantitative evaluation, this section examines the influence of image quality cataract diagnosis through structured interpretability analyses. Qualitative visualization results are also provided to further strengthen the effectiveness of the proposed framework. Finally, this section discusses key technical challenges identified during experimentation and explains how the Cateereen dataset effectively mitigates these issues.
Models/ Classes
Mlodes//Classcs
Res18
VGG-16
Res34
M M_2
V2
E-b4
DN121
Swin
VIT
Quality
Good
0.00
0.00
0.00
0.00
0.07
0.00
0.00
0.07
Acceptable
0.92
0.93
0.93
0.92
0.91
0.92
0.92
0.91
Poor
0.12
0.00
0.12
0.19
0.13
0.18
0.05
0.12
Illumination Typc
Diffuse
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
Direct Focal
0.94
0.94
0.93
0.94
0.94
0.95
0.94
0.93
Retro
0.92
0.92
0.91
0.92
0.92
0.93
0.91
0.90
Diagnosis
Normal
0.60
0.66
0.60
0.64
0.64
0.65
0.65
0.64
Cataract
0.78
0.79
0.80
0.79
0.79
0.80
0.81
0.81
Others
0.93
0.94
0.95
0.94
0.94
0.95
0.94
0.92
Cataract Type
NA
0.61
0.62
0.67
0.64
0.62
0.63
0.67
0.58
Nuclear
0.74
0.74
0.77
0.75
0.74
0.75
0.75
0.72
Cortical
0.36
0.30
0.38
0.38
0.45
0.52
0.40
0.13
PSC
0.13
0.08
0.16
0.11
0.25
0.13
0.32
0.00
PP
0.91
0.92
0.92
0.91
0.90
0.92
0.93
0.88
Others
0.11
0.21
0.10
0.11
0.21
0.11
0.17
0.13
Cataract Grade
NA
0.77
0.80
0.76
0.76
0.77
0.79
0.79
0.80
Mild
0.65
0.69
0.66
0.66
0.67
0.67
0.68
0.70
Severe
0.23
0.20
0.21
0.21
0.19
0.19
0.28
0.15
Table 3: Fl Score comparison of independent label prediction across various backbones under unimodal setting. (Here Res: ResNet, __V2: V2: MobileNet-V V2, E-b4: EfficientNet-B4, DN121: Pseudophakia, PSC:
DenseNet121, PP:
Posterior Subcapsular Cataract, NA: Not Applicable)
6.1 Independent Label Prediction under unimodal and multimodal settings
This section presents the Fl scores for each diagnostic task within the unimodal and multimodal settings. The metadata combined with the clinical tasks, such as cataract diagnostic classification and severity prediction. Several tasks exhibit near-zero F1 scores across models. This behaviour arises from extreme class imbalance, where some classes represent only small fraction of the dataset. These outcomes should be interpreted reflecting data scarcity rather than inherent inability of the models to learn discriminative features. Table summarizzes the Fl scores for each diagnostic attribute across various popular deep learning backbone architectures utilized for medical imaging classification tasks. Complementing these results, Figure visually illustrates the mean precision and recall achieved by each model, facilitating intuitive comparisons. The results consistently indicate an inverse correlation between the complexity the classification task and model performance. For simpler classification tasks, such as illumination type classification, nearly all models achieved excellent F1 scores (0.90-0.98). Particularly, the Diffuse and Direct Focal illumination types were classified with very high confidence, reflecting their distinctive visual characteristics. However, the Retro
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Models
Classes
Res18
VGG-16
Res34
M_2 V2
E-b4
DN121
Swin
VIT
Diagnosis
Normal
0.67
0.67
0.66
0.63
0.68
0.67
0.69
0.66
Cataract
0.78
0.81
0.78
0.78
0.82
0.80
0.80
0.81
Others
0.94
0.97
0.94
0.95
0.96
0.96
0.95
0.94
Cataract Type
NA
0.65
0.64
0.66
0.61
0.62
0.66
0.62
0.65
Nuclear
0.73
0.74
0.74
0.75
0.75
0.72
0.41
0.70
Cortical
0.22
0.33
0.38
0.34
0.52
0.43
0.09
0.09
PSC
0.18
0.26
0.12
0.14
0.31
0.33
0.06
0.11
PP
0.92
0.94
0.92
0.93
0.94
0.94
0.74
0.89
Others
0.35
0.30
0.33
0.30
0.15
0.24
0.13
0.37
Cataract Grade
NA
0.77
0.80
0.78
0.76
0.80
0.78
0.79
0.77
Mild
0.63
0.68
0.61
0.67
0.65
0.66
0.71
0.62
Severe
0.27
0.27
0.27
0.25
0.33
0.26
0.25
0.30
Table 4: F1 Score comparison of independent label prediction across various backbones under multimodal setting (Here Res: ResNet, M V2: M_ MobileNet-VV2 E-b4: EfficientNet-BA, DN121: DenseNet121, PP: Pseu- dophakia, PSC: Posterior Subcapsular Cataract, NA: Not Applicable)
illumination type showed marginally lower performance 0.93),,.93), possibly attributable to subtler visual cues and its similarity to direct focal illumination. Accordingly, results for severely under-represented classes should be interpreted as preliminary baselines rather than stable estimates of achievable task performance.
Table ! summarizes the Fl scores for the individual tasks under the multimodal setting. These results establish a bascline for multimodal learning across individual tasks. Compared to an unimodal setting, these models show improved performance diagnostic classification and severity grading across most backbones. This indicates that patient-level contextual information complements visual features in the diagnostic tasks. These results highlight the complementary role of metadata in improving the robustness of the clinical relevance of the proposed CatScreen dataset.
6.1.1 Image Quality Assessment
In the task of image quality assessment, the Acceptable class consistently achieved robust Fl scores ranging between 0.91 and 0.93. Conversely, Good and Poor categorics showed significantly weaker performance, often with near-zero scores. This result highlights a notable data imbalance favoring the Acceptable class, emphasizing the need for data augmentation strategies class-balanced loss functions to enhance model sensitivity towards minority classes.
6.1.2 Diagnostic Classification
Diagnostic classification demonstrated moderate-to-high accuracy across most models. The Calaraet class notably exhibited high F1 scores (0.78-0.81), attributable distinct visual features and adequate repre- sentation in the dataset. In contrast, the Normal class showed relatively lower scores (0.60 0.66), reflecting the challenges of distinguishing early-stage cataracts from healthy eyes due to subtle visual overlaps. The Others category demonstrated strong performance (0.92-0.95) despite minor variations, suggesting some degree of heterogeneity in these conditions and highlighting opportunities for further data enrichment.
The multimodal models show an improved performance across all classes. The Normal class shows an increase in the F1 score from 0.60-0.66 in the unimodal sctting to 0.63-0.60 with metadata. The Catoract and Others class also maintains strong performance. These results highlight an increase in diagnostic sensitivity without degrading performance.
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Models//lassee
Res18
VGG-16
Res34
M V2
E-b4
DN121
Swin
VIT
Image Quality
Good
0.07
0.05
0.00
0.00
0.00
0.00
0.00
0.02
Acceptable
0.85
0.90
0.88
0.87
0.80
0.88
0.90
0.90
Poor
0.24
0.16
0.22
0.19
0.19
0.18
0.11
0.12
Diagnosis
Normal
0.68
0.66
0.64
0.63
0.65
0.67
0.68
0.66
Cataract
0.73
0.70
0.73
0.74
0.76
0.71
0.79
0.76
Others
0.93
0.94
0.94
0.93
0.92
0.94
0.93
0.94
Cataract Type
NA
0.29
0.26
0.29
0.36
0.34
0.33
0.30
0.38
Nuclear
0.74
0.75
0.41
0.53
0.62
0.69
0.27
0.68
Cortical
0.33
0.31
0.29
0.30
0.32
0.26
0.14
0.09
PSC
0.04
0.24
0.10
0.17
0.09
0.25
0.09
0.08
PP
0.08
0.67
0.06
0.16
0.23
0,12
0.06
0.07
Others
0.00
0.00
0.00
0.10
0.00
0.00
0.00
0.00
Cataract Grade
NA
0.27
0.19
0.36
0.33
0.38
0.24
0.18
0.33
Mild
0.62
0.77
0.73
0.74
0.67
0.69
0.65
0.69
Severe
0.31
0.19
0.29
0.28
0.37
0.29
0.31
0.30
Table 5: Fl-scores for the multimodal Structured Sequential Analysis (SSA) pipeline across backbones for clinical tasks. (Here Res: ResNet, M V2: MobileNet-V22 E-b4: EfficientNet-B4, DN121: DenscNet121, PP: Pseudophakia, PSC: Posterior Subcapsular Calaract, NA: Not Applicable)
6.1.3 Cataract Type Classification
Cataract subtype classification proved significantly challenging due to subtle visual distinctions and class imbalance. Pseudophakia (PP) achieved consistently high Fl scores (0.88 0.93) due to visually dis- tinet characteristics. However, subtypes like Posterior Subcapsulor Cataract (PSC) and Cortical performed poorly (F1 scores ranging from 0.00-0.32 and 0.13-0.52 respectively), highlighting difficulties arising from their subtle visual patterns and limited training data. The Nuclear subtype showed intermediate to strong performance (0.72-0.. 0.77), benefiting from central opacity features, while the Others subtype presented highly inconsistent results (0.10 0.21), primarily driven by data scarcity.
Cataract subtype classification, such as Pseudophakia and Nuclear Cataracts, maintains good performance in both unimodal and multimodal settings. However, predicting more challenging cases, such as PSC and Cortical classes, shows good improvements in the multimodal setting. Due visual ambiguity and class imbalance, fine-grained subtype classification remains challenging and will require more robust models that handle such limitations in future work. In particular, the results for PSC and Others subtypes should be viewed primarily as reference points for future clas-imballncc--warr methods.
6.1.4 Cataract Severity Grading
Cataract severity grading emerged as particularly challenging, with Severe cases consistently showing poor F1 scores (0.15-0.28), reflecting significant class imbalance and limitod represcntation. The Not Applica- ble (NA) category, representing non-cataract cases, achieved higher scores (0.76 0.80), and the Mild class produced moderate scores (0.65-0.70). These results emphasize the need for richer datasets that include adequate representation of severe cataract cases and advanced model architectures to accurately capture severe pathological patterns.
The impact metadata is more pronounced in predicting cataract severity grading. Compared to the unimodal bascline, the multimodal framework achieves higher F1 scores for the Severe class, increasing from 0.15-0.28 in the unimodal setting to 0.25-0.33 in the multimodal setting. This improvement highlights that patient-level contextual information complements visual features and that severity grading is influenced by
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Efficient
SSA
Direct
Acc
Macro Fl
Acc
Macro Fl
Quality
0.22
0.12
0.22
0.12
SSA Poor
Diagnosis
0.83
0.81
Grade
0.57
0.38
Quality
0.87
0.31
0.87
0.31
SSA Acceptable
Diagnosis
0.78
0.81
0.78
0.81
Grade
0.59
0.46
0.54
0.34
Quality
0.25
0.13
0.25
0.13
GT Poor
Diagnosis
0.79
0.68
0.79
0.66
Grade
0.56
0.40
0.49
0.32
Quality
0.86
0.31
0.86
0.31
GT Acceptable
Diagnosis
0.78
0.81
0.79
0.81
Grade
0.59
0.46
0.55
0.35
Table 6: Impact of image quality on quality assessment, cataract diagnosis, and severity grading using EfficientNet-B4 under SSA and Individual label prediction (Direct) settings.
factors beyond visual appearance. Nevertheless, due to class imbalance, the overall performance gains remain constrained. This underscores the need for more balanced and class-aware learning strategies to build robust models.
6.2 SSA Framework with Structured Evaluation and Post-hoc Interpretability Analysis
To examine the behavior of the SSA formulation in clinically structured inference setting, implement conditional sequential inference pipeline that combines independently trained single-task models to mimic the realistic diagnostic workflow. During training, the best model for each task is selected based on the F1 Score. Experimental results are presented in Table 5 The pipeline operates as follows: image quality is assessed, and poor-quality images are rejected for further processing. Diagnostic classification is subsequently performed using both image and metadata inputs. Images classified as normal or as others are excluded from the severity assessment, This conditional execution reflects real-world cataract screening, where early stages determine whether further diagnostic analysis is required.
Since the number of images passing the quality gale varies across backbone architectures, we evaluate the SSA framework using test sets consisting of SSA-Acceptable and SSA-Poor images. This set comprises images that pass the quality gate or are rejected, respectively, in the pipeline. To maintain clarity, we have presented results on the best-perforning model, EfficientNet-B4.
Table presents the impact of quality gate on subsequent task evaluations. Initial SSA-based evaluations revealed that performance differences between SSA-Poor and SSA-Acceptable subsets were not always con- sistently degradative across downstream tasks. This is mainly due to the severe class imbalance in image quality labels, which limits the reliability of the quality assessment model in certain cases. As result, there is gap between predicted and annotator-provided quality labels. To validate the underlying clinical hypoth- esis that poor-quality images degrade diagnostic and grading performance, we repeated the analysis using ground-truth quality labels. This ground-truth-based evaluation demonstrates a consistent and expected degradation in both cataract diagnosis and severity grading performance for poor-quality images.
To provide additional post-hoc insight into model behavior, conducted GradCAM analyses for image- based predictions and SHAP analyses for metadata contributions. Figure 6a presents the GradCAM for the correctly classified and misclassified samples. The correctly classified samples show that the models focus on relevant lens regions, whereas misclassified cases exhibit diffuse or misplaced attention patterns that focus on peripheral or non-diagnostic regions. In parallel, Figure 6bb presents the top features that influence the cataract diagnosis and severity grading tasks. The SHAP-based analysis elucidates that diagnostic classi- fication relies on surgical history-related features. This reflects the strong association with post-operative lens status and non-cataract conditions. In contrast, cataract severity grading exhibits greater sensitivity
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Correctly classified
Misclassified Samples
Cataract
Normal
Other
(a)
Cataract Diagnosis
Severity Grade
Cataract Surgery
VA (Unaided): Normal
Past Eye Surgery
Blurred Vision
Age
Age
Blurred Vision
VA (Aided): Normal
VA (Unaided): Normal
History of Hypertension
VA (Aided): Normal
History Eye Problem
Gender
(Unaided]: Moderate
History Hypertension
Past Eye Surgery
History of using Spectactes History of Eye Problem
sic
History of Systemic Disease Gender
6.99
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Mean Absolute SHAP Value
Mean Absolute SHAP Value
(b)
Figure 6: Presents (a) GradCAM visualizations for correctly classified and misclassified samples in the multi- modal SSA framework using Efficient and (b) SHAP value plots highlighting the relative importance of patient-level metadata features for cataract diagnosis and severity grading.
functional vision metrics, such as unaided visual acuity, blurred vision, and age. This observation aligns with clinical practice, in which visual function is a key indicator cataract disease progression. These anal- yses provide qualitative, post-hoc evidence about model attention and feature importance, but they should not be interpreted definitive validation of the model's reasoning process or as formal proof of superior interpretability of the SSA formulation.
6.3 Multitask Learning Framework
Tables and g summarize the F1 scores for each diagnostic attribute within the multitask learning frame- work, where multiple tasks are predicted simultancously from shared visual representations in unimodal and multimodal settings, respectively. To assess the impact of metadata within the multitask learning paradigm,
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Classess
Res18
VGG-16
Res34
M
V2
E-b4
DN121
Swin
VIT
Quality
Good
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Acceptable
0.93
0.93
0.93
0.92
0.93
0.93
0.93
0.93
Poor
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.10
Illumination
Type
Diffuse
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
Direct Focal
0.95
0.94
0.93
0.94
0.93
0.94
0.94
0.94
Retro
0.92
0.92
0.92
0.92
0.90
0.91
0.92
0.91
Diagnosis
Normal
0.63
0.66
0.64
0.63
0.60
0.65
0.67
0.66
Cataract
0.79
0.81
0.80
0.80
0.79
0.80
0.80
0.82
Others
0.91
0.91
0.92
0.89
0.91
0.92
0.94
0.92
Cataract Type
NA
0.65
0.66
0.63
0.64
0.61
0.65
0.67
0.67
Nuclear
0.76
0.75
0.76
0.76
0.74
0.76
0.75
0.78
Cortical
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.50
PSC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PP
0.90
0.92
0.89
0.87
0.89
0.91
0.92
0.91
Others
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Cataract Grade
NA
0.79
0.80
0.78
0.78
0.78
0.79
0.81
0.81
Mild
0.69
0.71
0.69
0.71
0.70
0.70
0.71
0.74
Severe
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.09
Table 7: Fl Score comparison of multiple labels across various backbones feature extractors for multitask learning approach. (Here Res: ResNet, M_ MobileNet-V2 V2, F-b4: FfficientNet-BAd, DN121: 1enseNet121, PP: Pseudophakia, PSC: Posterior Subcapsular Cataract, NA: Not Applicable)
we also performed experiments for a multimodal multitask setting. We included metadata for clinically rel- evant tasks, and other tasks remain image-only, reflecting their dependence on acquisition conditions rather than patient context. Experimental results demonstrate increased performance gains in multimodal settings.
6.3.1 Image Quality Assessment and Illumination Type Classification
In multitask learning, image quality prediction again showed robust performance the Acceplable cate- gory (FI: 0.92 0.93). Conversely, the Good and Poor categories exhibited critically low performance (F1 near 0), highlighting persistent challenges due to class imbalance. Interestingly, Vision Transformer (ViT) demonstrated slight improvement in the Poor category (F1: 0.10), emphasizing the potential of transformer architectures for capturing subtle visual differences. Further, illumination type classification demonstrated exceptional performance across all models, achieving nearly perfect scores for the Diffuse category (F1: 0.98). Strong performances were also recorded for Direct Focal and Retro illumination classes, with F1 scores ranging from 0.90 to 0.95, highlighting the consistency and reliability of models in handling clearly distinguishable visual features.
6.3.2 Diagnostic Classification and Cataract Type Classification
Diagnostic classification within the multitask framework again yielded strong outcomes for the Cataract (F1: 0.79-0.82) and Others (F1: 0.91-0.94) classes, reflecting their visually distinct and adequately represented nature. The Normal calegory continued to pose challenges (F1: 0.60 0.67), reiterating difficulties in diller- entiating healthy from early pathological states due to subtle visual overlaps. Next, subtype classification presented considcrable challenges within the multitask framework. The Pseudophakia (PP) and Nuclear cataract classes showed commendable performances (F1: 0.87-0.92 and 0.74-0.78, respectively). In contrast, the Cortical, PSC, and Others subtypes consistently yielded near-zero scores, emphasizing the limitations
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Moodess/Classee
Res18
VGG-16
Res34
M MV2
E-b4
DN121
Swin
VIT
Quality
Good
0.00
0.00
0.09
0.02
0.00
0.07
0.00
0.05
Acceptable
0.89
0.89
0.89
0.87
0.88
0.88
0.88
0.88
Poor
0.28
0.20
0.25
0.24
0.25
0.22
0.24
0.22
Illumination
Type
Diffuse
0.98
0.98
0.98
0.98
0.98
0.98
0.98
0.98
Direct Focal
0.93
0.94
0.94
0.93
0.94
0.93
0.94
0.93
Retro
0.90
0.92
0.91
0.91
0.92
0.91
0.91
0.90
Diagnosis
Normal
0.65
0.67
0.64
0.63
0.66
0.68
0.67
0.64
Cataract
0.79
0.80
0.78
0.78
0.77
0.80
0.77
0.78
Others
0.91
0.96
0.93
0.94
0.95
0.96
0.95
0.92
Cataract Type
NA
0.67
0.65
0.65
0.64
0.65
0.67
0.64
0.62
Nuclear
0.70
0.74
0.71
0.70
0.71
0.72
0.75
0.69
Cortical
0.38
0.34
0.30
0.34
0.33
0.34
0.41
0.16
PSC
0.09
0.20
0.00
0.23
0.24
0.29
0.20
0.00
PP
0.91
0.94
0.91
0.93
0.93
0.93
0.94
0.91
Others
0.09
0.24
0.22
0.36
0.11
0.17
0.31
0.22
Cataract Grade
NA
0.79
0.78
0.77
0.76
0.79
0.79
0.79
0.78
Mild
0.63
0.67
0.61
0.61
0.66
0.69
0.67
0.60
Severe
0.23
0.20
0.21
0.28
0.20
0.21
0.34
0.30
Table 8: Fl Score comparison of multiple labels across various backbones feature extractors for multimodal multitask learning approach. (Here Res: ResNet, V2: MobileNet-V2 V2, E-b4: EfficientNet-B. DN121:
121, PP: Pseudophakia, PSC: Posterior Subcapsular Cataract, NA: Not Applicable)
imposed by subtle visual differences, insufficient data, and high intra-class variability. The ViT architecture, however, demonstrated notable improvement for the Cortical subtype (F1: 0.50), confirming the potential of transformer- models in finc-grained classification tasks.
The multimodal multitask framework demonstrates a consistent. improvement, over the unimodal baseline. The Fl score of the Normal class increases from approximately 0.60 0.67 in the unimodal setting 0.63 0.68 with metadata across most backbones. For cataract type classification, there is similar trend of consistent performance improvement across classes. Fl scores of cataract subtypes, such Cortical, improve from near-zero values in the unimodal multitask sctting to approximately 0.30-0.41 across several backbones, while PSC improves from near-zero to approximately 0.20 0.29 under the multimodal multitask framework. The performance of other classes remains stable or slightly improves, indicating that metadata serves as complementary information to visual information without affecting the prediction of visually distinctive classes.
6.3.3 Cataract Severity Grading
Severity grading results mirrored challenges observed in the sequential analysis. The Not Applicable (NA) and Mild classes performed reasonably well (F1: 0.70-,.81), while the Severe category remained problematic with consistently poor scores (near-zero Fl scores). The slight improvement observed in the Severe category by ViT (F1: 0.09) further supports transformer architectures' capability to manage complex visual variations belter than traditional models. The inclusion of metadata helps to improve the performance of this task. Compared to the unimodal multitask baseline, the multimodal multitask framework improves the Fl score for the Severe class from near-zero values (2 0.09) approximately 0.20-0.34 across different backbone architectures. These results demonstrate the impact of metadata the performance of overall diagnostic tasks.
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6.4 Future Research Directions
CatSereen's design explicitly supports research beyond supervised classification. While the present work focuses baseline supervised benchmarks on the clean set and selected multimodal experiments, CatScreen is intentionally designed to support broader range of future rescarch directions. The noisy-labeled subset enables benchmarking of label-noise robust algorithms under realistic annolation conditions. The unlabeled subset of 5,481 images facilitates self-supervised pretraining and semi-supervised learning approaches. The anatomical segmentation annotations support development of interpretable models with explicit anatomical grounding. These components collectively position CatScreen as a comprehensive resource for advancing robust, generalizable, and interpretable cataract screening solutions.
Specifically, the noisy-labeled subset can be used to evaluate methods such co-teaching, confident learning, and noise-robust loss functions. The unlabeled subset enables exploration of self-supervised pretraining frameworks (e.g., SimCLR, DINO, MAF) that can improve feature representations for downstream tasks. Comprehensive evaluation of these components represents natural extensions of this work.
6.5 Technical Challenges and CatScreen's Contributions
Our findings identify several promising directions for future research to address current limitations. Leverag- ing sclf-supervised pretraining tochniques can enhance models' abilitics to extract meaningful features from unlabeled ophthalmic data. Moreover, integrating advanced attention mechanisms and multi-scale feature refinement strategies could significantly boost model sensitivity to subtle visual distinctions, critical for effec- tive fine-grained classification. These methods can help models better discriminate between visually similar ophthalmic conditions, potentially improving performance on fine-grained and clinically relevant screening tasks.
The multitask learning framework specifically requires careful balancing between shared representations and spsc-ip fine-tuning to mitigate negative transfer and improve overall task performance. Implementing dynamic weig-tigg or uncertainty-based loss re-weighting strategies could address these challenges ef- fectively. Conversely, the structured sequential analysis framework would benefit from explicitly modeling inter-task dependencies. Figure showcases the sample result images with true and predicted labels that are misclassified due to poor-quality images. In sequential causal models, early-stage errors cascade forward and cause misclassifications in subsequent tasks that depend on these intermediate predictions. This emphasizes the critical need for robust handling of quality variations to mitigate cascading errors in sequential decision models. Approaches such as cascaded conditioning, multi-stage refinement modules, or cross- tossstask attention mechanisms could leverage shared clinical features while maintaining modularity and improving diagnostic accuracy.
A key limitation of the proposed benchmark is the severe class imbalance present in certain classes, which limits the interpretability of the performance metrics for the rare classes. While this imbalance mirrors real- world clinical prevalence, it limits the reliable assossment of the model behaviour for these classes. Future research will address class imbalance using techniques like targeted data collection, data sugmentation, syn- thetic oversampling, or focal loss methods, which could significantly improve sensitivity to minority classes, particularly severe cataract cases and extreme image quality categories. Further rescarch into advanced at- tention mechanisms, ensemble approaches, or hierarchical classification strategies specifically targeted toward challenging cataract subtypes, such as Cortical and PSC, could better capture nuanced visual distinctions. Lastly, explicitly evaluating model robustness through the noisy-labeled subset by leveraging semi-supervised and robust learning approaches would enhance real- wol-world generalizability and reliability.
To support these research directions effectively and address identified technical challenges, Cateereen provides several key contributions and innovative features:
Class Imbalance and Minority Class Performance: Both SSA and MTL frameworks exhibited significantly reduced performance on minority classes. CatScreen preserves realistic class distribu- tions and therefore provides useful benchmark for future work on minority-class performance and class-imbalance-awaal learning.
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Diagnosis
True Label: Predicted Label
Cataract Others
Cataract Normal
Cataract Normal
Cataract Normal
Cataract Normal
Grading
True Label Predicted Label
Mild
Severe
Mild
Mild
Mild
NA
Mild
NA
NA
NA
Figure 7: Presents the sample result images of poor-quality images causing misclassification across diagnosis and severity grade labels
Fine-grained Classification: Differentiating subtle visual distinctions among cataract subtypes and severity levels remains challenging. CatScreen's fine-grained annotations and precise labeling enable models to learn nuanced, clinically relevant fine-grained distinctions, thereby supporting future work on subtype discrimination and anatomically grounded analysis.
Robustness to Annotation Noise: Clinical datasets inherently contain annotation inaccuracies. CatScreen includes a Noisy Set specifically designed to develop and evaluate models robust against imperfect annotations, thus aligning more closely with real-world clinical practice.
Utilizing Unlabelled Data for Enhanced Representation Learning: To boost generalization and robustness, CatScreen offers substantial Unlabelled Set. This supports the development of self-supervised, semi-supervised, and active learning techniques, enabling models to utilize extensive unlabeled data for improved feature extraction and stronger generalization.
7 Conclusion
CatScreen advances ophthalmic diagnostics by offering a richly annotated, large-scale slit-lamp image dataset tailored specifically for addressing real-world challenges in cataract screening. captures comprehensive range of clinically relevant annotations, including image gradability, illumination types, cataract subtypes, and severity grades, enabling the creation of highly interpretable and clinically meaningful AI solutions. Be- yond traditional supervised learning, CatScreen dataset uniquely incorporates noisy-labelled and unlabeled data subsets, thereby facilitating research into robust self-supervised and semi-supervised learning strategies reflective of practical clinical scenarios. The detailed patient metadata further onhances its utility by sup- porting the integration holistic clinical contexts into diagnostic models. this work, we present baseline evaluations under independent, structured sequential, and multitask prediction settings both unimodal and multimodal configurations. These experiments provide initial reference results and show that metadata improve performance for selected diagnostic tasks, while also highlighting persistent challenges such severe class imbalance, minority-class sensitivity, and fine-grained subtype discrimination. We anticipate that CatScreen will inspire extensive innovation, significantly accelerating the adoption of AI-driven eye care solutions and improving global calaract screening and management. Furthermore, the inclusion of noisy- labeled, unlabeled, and regmentation-annotaee subsets provides researchers with resources to explore robust. learning, semi-supervised mothods, and anatomically-gromi approaches future studics.
8 Broader Impact Considerations
As a medical imaging dataset intended to support calaract screening in resouroe-limited settings, several broader considerations warrant discussion. First, the class imbalance present in CatScreen, particularly the underoppesentation of severe cataract cases (9.6% of the datasot), may result in models with lower sensitivity for the most clinically urgent cases. Deployment strategics should account for this limitation through appropriate threshold calibration ensemble approaches.
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Second, while patient metadata enhances diagnostic performance, it also introduces potential for demographic biases. Models leveraging metadata should be evaluated across demographic subgroups (age, gender, diabetic status) to ensure equitable performance before clinical deployment.
Finally, CatScreen is designed to support screening and triage workflows rather than replace clinical judg- ment. Any real-world deployment should maintain appropriate human oversight by trained healthcare pro- fessionals, with Al-assisted predictions serving decision support rather than autonomous diagnosis.
Acknowledgment
This work was supported by iHub-Drishti, the Technology Innovation Hub at IIT Jodhpur.
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26


Reviews:
[{"review_id": "uoa08L0Fzp", "reviewer": "Reviewer_W7VZ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This manuscript addresses a clinically meaningful problem. Cataracts are a leading cause of reversible blindness worldwide, particularly in underserved and remote regions, where access to ophthalmic care is limited. The development of a low-cost, scalable screening solution using slit lamp imaging is therefore quite relevant, and the creation of a large, publicly available dataset has the potential to significantly advance research and innovation in this area.", "claims_and_evidence_yn": "No", "claims_explanation": "The use of a portable slit lamp camera integrated with a smartphone is an interesting design choice, particularly given the goal of expanding access to care in resource-limited settings. However, image quality from smartphone-based slit lamp systems may be inferior to that obtained with traditional tabletop slit lamps. While portability offers clear advantages for deployment in remote areas, the potential trade-off in image quality should be more explicitly discussed, including how this may affect cataract detection, grading performance, and generalizability of the proposed models.\n\nThere appears to be some ambiguity regarding expert annotation. The abstract states that images were annotated by “ophthalmology experts,” yet the methods section describe only one ophthalmologist (a retina specialist), with the remaining annotators being optometrists. This distinction is important and should be clarified for transparency. In addition, it would be helpful to explain the rationale for involving a retina specialist for expert validation rather than an anterior segment surgeon, given that cataract diagnosis and grading are primarily within the anterior segment domain.\n\nSeveral points in the ground-truth labeling scheme would benefit from clarification. Under cataract type, it’s unclear whether aphakia is included within the “Others” category. Additionally, under cataract grading, the label “Mild” may be misleading, as grading systems often combine early disease into a “Mild/Moderate” category; the authors may wish to clarify or adjust this terminology to better align with established clinical conventions. In the anatomical and pathological annotation section, the term “conjunctival hemorrhages” appears to be used; it would be helpful to confirm whether this refers specifically to subconjunctival hemorrhages.", "audience_yn": "Yes", "audience_explanation": "would be of interest of anyone working at the intersection of AI and ophthalmology", "requested_changes": "Figure 3 depicts a slit lamp photograph obtained through a miotic and likely undilated pupil.  From a clinical standpoint, pupil dilation is generally considered the gold standard for comprehensive cataract assessment, as it allows visualization of the entire lens. The manuscript should clarify whether dilating drops were administered prior to image acquisition and, if not, discuss how undilated imaging may limit cataract evaluation and grading accuracy.\n\nThe manuscript notes that opticians were involved in ocular imaging, which raises questions regarding scope of practice. In many regions, opticians primarily function as spectacle or optical vendors and are not typically trained or credentialed to perform ocular imaging. The authors should clarify whether opticians in their region are formally trained to acquire slit-lamp photographs and how imaging consistency and quality were ensured across operators.\n\nFigure 6 illustrates examples of poor-quality images in which the slit beam is not consistently positioned on the lens, and the lens is not always centered within the image. This raises concerns about image acquisition variability, potentially due to patient movement or operator technique. It would help if the authors include the numbers or percentages of photos that are good vs. acceptable vs. poor quality.\n\nFrom a medical and clinical research perspective, abstracts typically include quantitative performance metrics to allow readers to quickly assess the effectiveness of the proposed method. In this abstract, no specific numerical results (such as AUC, etc.) are reported for the proposed AI frameworks. As a result, it is difficult to gauge the  actual performance or clinical utility of the approach based on the abstract alone. The authors may wish to clarify whether the omission of performance metrics is intentional and standard for this type of dataset-focused contribution, or whether representative results could be included. Providing key quantitative outcomes would help readers better understand how effective the proposed methods are and how they compare to existing approaches.", "broader_impact_concerns": "none", "additional_comments": "none", "extra_scores": {}, "extra_text": {}}, {"review_id": "joMI6xbscM", "reviewer": "Reviewer_Tvtv", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces CatScreen, a large-scale, multimodal slit-lamp image dataset for cataract screening, comprising a large number of images captured using a low-cost portable device. Relative to prior cataract datasets, CatScreen is distinguished by its breadth of clinically relevant annotations per image, including image gradability and quality, illumination type, diagnostic category (normal / cataract / other), cataract subtype, and severity grading. In addition, the dataset includes subject-level demographic and clinical metadata, a noisy-labeled subset, an unlabeled subset, and segmentation masks for key ocular structures on a subset of images. The paper also presents two baseline modeling paradigms and benchmarks a range of CNN and transformer backbones. The primary contribution is therefore the dataset itself, positioned as a comprehensive benchmark for developing and evaluating clinically grounded cataract screening models, rather than new machine learning methodology.", "claims_and_evidence_yn": "No", "claims_explanation": "When contextualized against the existing literature, the dataset contribution is meaningful. Prior slit-lamp datasets typically involve on the order of hundreds of subjects or a few thousand images, often limited to a single cataract subtype or severity axis. In contrast, CatScreen’s scale, labeling, and subtype diversity present a richer data source. \n\nThe data collection protocol, ethical approvals, and three-tier annotation process are described in sufficient detail to support claims of annotation quality and clinical relevance. The benchmark experiments also consistently reflect known challenges in ophthalmic imaging, particularly severe class imbalance and the difficulty of fine-grained subtype and severity classification, lending credibility to the reported results.\n\nHowever, several claims are stronger than what is empirically demonstrated. The paper emphasizes interpretability, causal structure, and robustness to real-world noise, but the experimental section does not convincingly validate these properties. The structured sequential analysis framework is motivated as causally informed, yet no quantitative or qualitative analysis demonstrates improved interpretability, reduced error propagation, or causal insight compared to standard multitask learning. Similarly, while noisy labels and unlabeled data are highlighted as key dataset components, they are not substantively used in the reported experiments. Thus, while the dataset’s potential is clear, some downstream claims remain aspirational rather than evidenced.", "audience_yn": "Yes", "audience_explanation": "This work should be of interest to researchers focused on medical imaging datasets, AI for ophthalmology, and dataset design for real-world clinical deployment, particularly in low-resource settings. Compared to existing cataract datasets, CatScreen offers a more comprehensive and realistic annotation schema that better reflects clinical workflows. However, the appeal is primarily to applied ML and medical imaging researchers rather than to those seeking new algorithmic or theoretical contributions in machine learning.", "requested_changes": "* While the structured sequential analysis framework is motivated as interpretable and causally aligned with clinical reasoning, the paper does not empirically demonstrate these benefits. At present, this framing remains largely conceptual. Either stronger evidence should be provided (e.g., error propagation analysis, clinician-aligned interpretability studies), or the claims should be softened.\n* The dataset’s most novel components: metadata, noisy labels, unlabeled data, and segmentation masks, are largely unused in the experiments. As a result, the benchmarks do not fully justify the emphasis placed on these features in the paper. Incorporating even preliminary experiments using metadata (it is unclear whether the experiments for the structured sequential analysis framework incorporate this) or noisy-label robustness would substantially strengthen the contribution.\n* Many tasks (e.g., quality “Good”/“Poor”, PSC subtype, severe cataract grading) yield near-zero F1 scores across almost all models. While this reflects real-world data distributions, the paper does not sufficiently discuss how such results should be interpreted or how future work might meaningfully address them. This severe class imbalance limits the interpretability of the benchmark.\n* Figure 5 is very low resolution, visually cluttered, and difficult to interpret. Axis labels are barely legible, legends are unclear, and the dense layout obscures meaningful comparison across models and tasks. Given that this figure is central to the empirical evaluation, it should be substantially redesigned or replaced with clearer tables.\n* Several figures (Figures 3, 5) could be simplified without loss of content. Additionally, these figures are very low resolution and hard to read. Some tables (e.g. table 3) duplicate information already described in the text.", "broader_impact_concerns": "No major ethical concerns are apparent. The paper could consider briefly acknowledging standard considerations relevant to medical screening datasets, such as the implications of class imbalance (particularly for severe cases), potential demographic biases when using patient metadata, and the importance of human oversight in any real-world deployment.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "PUEq1LjdeZ", "reviewer": "Reviewer_aGs3", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "# contribution\nThis paper presents CatScreen, a large-scale multimodal dataset for cataract screening. Its core contributions are: 1) providing a substantial dataset of ~18.6k images from 2.25k subjects, 2) partitioning the data into clean, noisy, and unlabeled subsets to support various learning paradigms, and 3) proposing and benchmarking two complementary AI frameworks (Structured Sequential Analysis and Multitask Learning) on the new dataset.\n\n# strength\n- The dataset is larger than many existing resources.\n-  It features fine-grained, multi-dimensional labels and includes pixel-level anatomical segmentations and patient metadata.\n\n# weakness\n- The paper does not detail how the noisy labels in the noisy-labeled set were generated. Without knowing the source or the nature of the noise, it is difficult to assess how well this subset truly emulates real-world clinical noise and, consequently, its utility for developing robust learning algorithms.\n\n- The experiments do not incorporate the collected metadata, leaving its actual benefit to the task unproven.\n\n- The claimed interpretability advantage of the sequential framework is not demonstrated with sufficient evidence.", "claims_and_evidence_yn": "No", "claims_explanation": "Refer to the weakness part:\n- The method for generating the noisy labels is not described, making its realism and utility unclear.\n- The actual benefit of metadata to the task remains unproven.\n- The claimed interpretability advantage of the sequential framework is not demonstrated with sufficient evidence.", "audience_yn": "Yes", "audience_explanation": "- The dataset is larger than many existing resources.\n- The dataset provides fine-grained annotations including image quality, cataract subtype, severity, and rare pixel-level anatomical labels.", "requested_changes": "As said in the weakness part, more detailed descriptions about 1)  how the noisy label set was generated, 2) the interpretability of the proposed framework, and 3) the empirical evidence about the benefit of metadata are needed.", "broader_impact_concerns": "None", "additional_comments": "/", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)