WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
Authors: Satoshi Tsutsui
Winnie Pang
Bihan Wen
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: white blood cells
morphological attributes
microscopic image
explainable AI
computer vision
TL;DR: We annotated 11 fine-grained attributes for each of the 10k microscopic images of white blood cells (WBCs) because no prior WBC dataset included them, despite their significance in explaining how to recognize different WBC types.

Abstract: The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics. For instance, an in-depth study of White Blood Cells (WBCs), a crucial component of our blood, is essential for diagnosing blood-related diseases such as leukemia and anemia. While multiple datasets containing WBC images have been proposed, they mostly focus on cell categorization, often lacking the necessary morphological details to explain such categorizations, despite the importance of explainable artificial intelligence (XAI) in medical domains. This paper seeks to address this limitation by introducing comprehensive annotations for WBC images. Through collaboration with pathologists, a thorough literature review, and manual inspection of microscopic images, we have identified 11 morphological attributes associated with the cell and its components (nucleus, cytoplasm, and granules). We then annotated ten thousand WBC images with these attributes, resulting in 113k labels (11 attributes x 10.3k images). Annotating at this level of detail and scale is unprecedented, offering unique value to AI in pathology. Moreover, we conduct experiments to predict these attributes from cell images, and also demonstrate specific applications that can benefit from our detailed annotations. Overall, our dataset paves the way for interpreting WBC recognition models, further advancing XAI in the fields of pathology and hematology.

WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
Satoshi Tsutsui,
Winnie Pang, Nanyang Technological University Singapore
Bihan Wen
{satoshi.. peatoshi.tsutsui, tsutsui, winnie. rinnie.pang, bihan bihan.wen) wententu.eeeeeeeeeeeeee edu. sg
Abstract
The examination of blood samples at microscopic level plays fundamental role in clinical diagnostics. For instance, an in-depth study of White Blood Cells (WBCs), crucial component of our blood, is essential for diagnosing blood-related diseases such as leukemia and anemia. While multiple datasets containing WBC images have been proposed, they mostly foeus on cell categorization, often lacking the necessary morphological details to explain such categorizations, despite the importance of explainable artificial intelligence (XAI) in medical domains. This paper seeks to address this limitation by introducing comprehensive annotations for WBC images. Through collaboration with pathologists, thorough literature review, and manual inspection of microscopic images, we have identified 11 morphological attributes associated with the cell and its components (nucleus, cytoplasm, and granules). We then annotated ten thousand WBC images with these attributes, resulting in 113k labels (11 attributes x 10.3k images). Annotating at this level of detail and scale is unprecedented, offering unique value to Al in pathology. Moreover, we conduct experiments to predict these attributes from cell images, and also demonstrate specific applications that can benefit from our detailed annotations. Overall, our dataset paves the way for interpreting WBC recognition models, further advancing XAI in the fields of pathology and hematology.
Introduction
The microscopic examination of human blood samples is essential in clinical diagnostics, providing valuable insights into wide range of health conditions. For example, white blood cells (WBCs) or leukocytes can serve as markers for various blood-related diseases in pathology and hematology. Accurate recognition of basic WBC types (neutrophils, eosinophils, basophils, monocytes, and lymphocytes) forms a fundamental component of pathological diagnostic methods M. used to identify various blood-related conditions such as leukemia and anemia [2]. Automating the cell recognition process significantly enhance diagnostic efficacy, and as such, multiple datasets have been proposed for WBC categorization [3, 4 5 7]. However, these existing datasets only annotate the cell types without any morphological characteristics of each cell, key explanatory factor in how hematologists recognize WBCs. This lack of information may limit the development explainable artificial intelligence (XAI) for cell image analysis, which is particularly significant in the medical domain where interpretability is crucial clinicians cannot reliably diagnose serious illnesses without clear explanations of model's conclusions.
In this paper, we introduce WBCAtt (Figure T). novel dataset for WBCs that densely annotated with morphological attributes (Figure 2). Each cell image, obtained from the PBC dataset (6), is annotated with Il attributes, which were determined through a comprehensive process involving discussions with pathologists, literature review, and manual inspection of cell images. One example
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Nucles
6-8 jm
Cell
Shape: Segmented bilobed
Nucleus
Size: Big
Nuclear cytoplasmic ratio: Low
Shape: Round
Chromatin density: Densely
Cytoplasm
Cytoplasm
Granule
Vacuole: No
Type: Round
Texture: Clear
Color: Red
Granule
Category: Eosinophil
Color: Light Blue
Granularity: Yes
(a) Cell Structure
(b) Microscopic Image
(c) Explanatory Attributes
Figure 1: We construct new dataset annotating 1I morphological attributes of microscopic images of white blood cells. Our set of attributes, grounded on medical literature, and plays critical role when hematologists recognize cells. Our dataset can facilitate the XAI in blood cell recognition.
of such attribute is the presence of small cytoplasmic holes known vacuoles, depicted in Figure (t)u). Vacuoles provide insights into the functional state cell and are typically observed in monocytes and neutrophils due their phagocytosis mechanism [8]. However, vacuoles found in other cell types, like lymphocytes and eosinophils, may indicate certain disorders [9] [Q]. Based on these references, we define the presence of cytoplasmic vacuoles as one of the attributes. In the end, we establish 11 attributes, each supported by at least one medical reference. These attributes are discussed based on their association with different cellular components: overall cell (Sec. (....3.I). nucleus (Sec. 3.2) cytoplasm (Sec. 3.3 and granule (Sec. 3.4). We annotated a total of 10,298 WBC images with these attributes, resulting best of our knowledge, this is the first public dataset to include such an extensive set of annotations, addressing the current gap in XAI for WBC analysis. Moreover, we conduct experiments to explore the capability of standard deep learning models in recognizing these attributes (Sec. 4.. Furthermore, we believe that our detailed attributes can improve the interpretability of machine learning models for recognizing WBCs and related blood disorders. To illustrate this, we showcase specific applications that can be developed using our newly-introduced dataset (Sec. 5)
113k labels (IT attributes x 10.3k images). To the
In summary, our contributions are as follows. We construct the first public dataset for WBCs annotated with comprehensive morphological attributes. addressing the current gap in developing interpretable models for WBC analysis. We also conduct experiments to automatically predict attributes from images, addition outlining specific applications. We hope that our dataset will foster advancements in XAI in pathology and hematology.
2 Related Work
White Blood Cell Recognition. WBC recognition is crucial for the diagnosis of various discases in hematology, leading numerous studies focusing on the development of automatic WBC clas-
sifiers [ 12 13 1 and the release of publicly available datasets [3] 4
5
7]. Prior to 2020, 6
public WBC datasets were limited size, containing only hundreds of images
3 5 which proved
insufficient for leveraging state-of-the th-thherrt art image classification models, such as Convolutional Neural Networks (CNNs). Recently, the introduction of larger datasets 6 7 containing around ten thousand images, has enabled subsequent studies [1I 12 13 [4] to develop novel deep learning models that improve classification performance. However, despite the inherent requirement for explainability in medical applications, no prior datasets annotate morphological attributes, which are essential explanatory factors when hematologists recognize WBCs. As such, our work aims to address this
significant yet overlooked aspect.
Attribute Datasets. Many attribute datasets have played a pivotal role in various fields of computer vision, inspiring diverse range of applications and fostering methodological advances. These include fashion (e.g., DeepFashion (15) for e-commerce applications [16), human faces (e.g.. CelebA [17)
for the security of facial recognition systems [18], animals (e.g., Caltech-UCSDD Birds [19] for zero- shot learning (20), and image aesthetics (e.g., AADB (2I) to investigate the inherent subjectivity of human artistic perceptions [22]. Attributes are often considered key interpretable elements in
computer vision systems. For example, interpretable autonomous driving systems can be developed
using explainable attributes 23 24. In medical domains, attributes of X-ray images [25] 26 and

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Cell Size
Cell Shape
(a) Big
(b) Small
(c) Round
(d) Irregular
Nuclear Cytoplasmic Ratio
Nucleus Shape
(e) High
(f) Low
(i) Segmented multilobed
(j) Segmented bilobed
(k) Unsegmented band
Chromatin Density
()) Unsegmented round
(m) Unsegmented Indented
(n) Irregular
(g) Densely
(h) Loosely
Cytoplasm Texture
Cytoplasm Color
(o) Clear
(p) Frosted
(q) Light Blue
(r) Blue
(s) Purple Blue
Cytoplasm Vacuole
Granule Color
(t) Yes
(u) No
(v) Pink
(w) Red
(x) Purple
Granularity
Granule Type
(y) Yes
(z) No
(aa) Small
(bb) Round
(cc) Coarse
Figure 2: Sample images of each attribute. See Sec. for descriptions.

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skin disease images [27 have been annotated to support the development of interpretable medical AI, sharing a similar motivation with our work. However, no existing dataset provides attribute annotations for the morphological characteristics of WBCs despite their importance in hematology. Our work addresses this gap by presenting densely-annotated dataset for WBC recognition.
3 WBCAtt: White Blood Cell Attribute Dataset
The attributes are categorized into four primary groups based on cell structure: overall cell, nucleus, cytoplasm, and granules, described in Sec. S.1. 3.4 respectively. The examples of these attributes are summarized in Figure 2 We utilized all images of typical WBCs from the PBC dataset [6]. which encompassed 1,218 basophils, 3,117 eosinophils, 1,420 monocytes, 3,329 neutrophils, and 1,214 lymphocytes. We annotated 11 attributes for these 10,298 images, resulting in 113,278 image-attribute pairs, with the distribution for each attribute shown in Figure
Attribute Definition Process. Since there was no formally established set of attributes or ontology, we initiated work with discussions with pathologists working in laboratory healthcare company that develops digital cell imaging analyzers (28). They provided five prominent attributes (see Coarse Morphological Attributes in Appendix) often used in identifying the five major WBC types, but also noted that the list was not exhaustive. Based on these initial keywords, we conducted a thorough review of relevant textbooks and research papers focusing on the morphological characteristics of WBCs. Subsequently, we refined the attribute set through further discussions with the pathologists and by manually inspecting approximately thousand WBC images. This process yielded a total of 1l attributes, each supported by at least one medical literature reference. While the samples we inspected were the five major WBC types from healthy individuals, expect the resulting attributes to sufficiently describe the morphological characteristics that may emerge in response to certain diseases, such as COVID-19 [29]. Moreover, these attributes provide pathologists with valuable insights into significant morphological abnormalities during microscopic examination, in accordance with the guidelines outlined in [30].
To ensure reliable annotations across over 10k images, we devised rigorous, iterative process involving pathologists, research scientists, and biomedical students who had strong knowledge of cell structures. In the initial stage, the students annotated the images, while being informed of the specific WBC category for each image. Following this, our research scientists meticulously examined each image and its assigned attributes, meaning that every image was inspected by at least two individuals. When ambiguities arose, we discussed with the pathologists who defined the attributes with us. ensuring consensus on labeling. Further details on the quality control are available in the Appendix. We assessed the reliability of our annotation process by replicating it on a subset of 1 1,000 images with different annotators. Out of the 11,000 attribute annotations, 10,569 were consistent with the original annotations, giving an agreement rate of approximately 10569/ 11999/11000 1000 ss 96.1% %. This high agreement rate demonstrates the robustness and reliability of our annotation process.
Annotation with Quality Control.
Cell size
Big
Small
Cell shape
Round
Irregular
Nucleus shape
Multi-lobed
Bllobed
Round Indented Irregular
NC ratio
High
Chromatin density
Densely
Loosely
Cytoplasm texture
Clear
Frosted
Cytoplasm color
Light Bluue
Blue
Purple Blue
Cytoplasm vacuole
No
Yes
Granularity
Yes
No
Granule color
Pink
Red
Purple
Nil
Granule type
Small
Round
Coarse
Nil
2000
6000
8000
10000
Images
Figure 3: The distribution of values per attribute, The distribution represents the results of annotating all typical WBCs from the PBC dataset, which is the image source we utilized. We did actively control or manipulate the distribution. See Sec. for the definitions of the attributes, and Figure for example images. See Appendix for the extract numbers.

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3.1 Cell
Cell Size. Cell size refers to the overall dimensions of WBC. Generally, the size of a WBC can often be indicative of its function, maturation stage, and activation state. For differentiating between various WBC types, lymphocytes usually have small cell size compared to other WBC types, such as monocytes and granulocytes [3]] Estimating cell size is typically done by comparing the WBC to neighboring red blood cells (RBCs; usually 6-8 jam) within the same blood sample, as RBCs provide a consistent reference for size comparison. A WBC is classified as big if its diameter is larger than twice the diameter of the RBCs. Examples of big and small cells can be found in Fig. (a)
Cell Shape. The cell shape of WBCs a significant morphological feature that offers insights into the cell type. WBCs can display range of shapes, from round to irregular, depending on the cell type and its interactions with the surrounding mieroenvironment, including red blood cells. Irregular shapes are more prevalent in neutrophils monocytes due to their unique functions or maturation stages [32 33 Additionally, WBCs can be irregular in shape due to their interactions with adjacent red blood cells. In our definition, WBCs with circular or oval shapes are categorized as round, while any other shapes are classified as irregular. Examples of round and irregular cells are shown in Fig. (d)..
3.2 Nucleus
Nucleus Shape. The shape of the nucleus can provide crucial information about the WBC types 34.
Segmented nuclei are typical characteristic of neutrophils and eosinophils. with neutrophils displaying a multilobed nucleus and eosinophils often exhibiting a bilobed nucleus (35). Band-shaped
nuclei can be found in immature neutrophils, also known as band neutrophils [36]. which are the intermediate stage in the maturation process of segmented neutrophils. Unsegmented nuclei can be observed in lymphocytes and monocytes, with lymphocytes typically having a round nucleus and monocytes having an indented or kidney-shaped nucleus [1. An "irregular" class is included to accommodate the nucleus shape that does not fall under the defined classes, often for the nucleus of a basophil or monocyte. In total, we have six nucleus shapes (Fig. 2 (n)): segmented- muttndddddddddddddd... seenented-bilobed unsegmented-bant
unsegmented-rouum
unsegmented-indenn
and irregular.
Chromatin Density. The density of nuclear chromatin, which refers to the compactness of chromatin within the nucleus, an important factor for distinguishing between different types of WBCs, such as lymphocytes and monocytes. In general. lymphocytes exhibit denser, heterochromatic nuclear chromatin, while the nucleus of monocytes appears as a "rough mesh", which contains more loosely packed, euchromatic chromatin 35 37). For the benefit of individuals who are not familiar with clinical terminology in using our annotations, we describe the chormatin density as loosely packed (euchromatic) and densely packed (heterochromatic) as illustrated in Fig. (g)
Nuclear cytoplasmic (NC) Ratio. The NC ratio refers to the proportion of the cell's volume occupied by the nucleus relative to the cytoplasm, and can provide valuable information regarding the cell type. WBC with a high NC ratio typically lymphocyte, while lower NC ratio is characteristic of other types. In this dataset, a WBC with an NC ratio greater than 0.7 38 is considered to have high NC ratio. This characteristic is often associated with thin rim of cytoplasm surrounding the nucleus. Fig. (f) depicts examples of WBCs with low and high NC ratios.
3.3 Cytoplasm
Cytoplasm Color. The color of the cytoplasm can offer valuable information regarding the cell type, as different WBCs often exhibit varying cytoplasm colors due differences in granule content and staining affinity. Furthermore, the cytoplasmic color can offer insights into granulocyte maturation, as it tends to vary across different stages of WBC development 39] In light of this, we have annotated
the cytoplasm colors ranging from light blue to purple blue 40 as shown in Fig. 2 (s))
Cytoplasm Texture. Cytoplasm texture also contributes to the classification of WBCs, as different cell types may exhibit unique textures due to variations in intracellular content, such as granules and other organelles. For example, tiny, dust-like purplish granules that sometimes appear in lymphocytes and monocytes give the cytoplasm a frosted or ground glass appearance [1] while the cells lacking these dist-like granules have a clear or transparent cytoplasm, shown in Fig, (o)
5

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Cytoplasm Vacuole. Discrete cytoplasmic vacuoles are often found in monocytes and sometimes in neutrophils due their phagocytosis mechanism [8], where pathogens and cell debris are engulfed and digested. The presence of these vacuoles can help identify cell types and provide insights into the cell's functional state. Vacuoles found in other cell types, such as lymphocytes [9] and eosinophils [10], might be indicative of certain diseases and can aid in the diagnostic process. Examples of cells with and without a cytoplasmic vacuole are shown in Fig. (0(u.
3.4 Granule
Granularity. In this dataset, granularity means the presence of prominent stainable cytoplasmic granules [35] that distinguish between granulocytes (neutrophils, eosinophils, basophils) and agranu- locytes (monocytes, lymphocytes). While granules are not entirely absent in agranulocytes, they are generally found in smaller quantities and are less noticeable compared to their presence in granulo- cytes [42]. Agranulocytes are categorized as having no granularity unless prominent granules are observed. Fig. (y) shows granulocyte (granularity: yes) and an agranulocyte (granularity: no).
Granule Color. Granule color a distinguishing factor among granulocytes. The granules within neutrophils, eosinophils, and basophils have different colors due to their distinct compositions, which reflect their unique immune functions. Neutrophil granules are typically pink, eosinophil granules are red, and basophil granules are purple 40 42) This attribute is particularly useful for distinguishing eosinophils from other granulocytes, as eosinophilic granules are uniquely stained red by eosin. This is due to the presence of cationic proteins within the eosinophilic granules, which bind to the cosin dye and give the cell its characteristic red color 43
Fig. 2 (v)()(() shows examples of them.
Granule Type. Granule type describes the morphological characteristics of granules in granulocytes. Neutrophils, cosinophils, and basophils each have their roles in the immune response, possessing distinct granule types filled with different substances. Neutrophils contain small, fine granules that are packed with antimicrobial proteins and enzymes, such as mycloperoxidase, lysozyme, and lactoferrin. The granules of the eosinophils are usually round and membrane- bound that compose of major basic protein. Basophils, on the other hand, possess conspicuous and coarse granules filled with histamine, heparin, and other mediators of inflammation [35] Fig, (an)b)(ccc) shows examples of them.
4 Attribute Prediction Experiments
While various applications are possible with our attributes (Sec. 5). preliminary question emerges: How well do standard visual recognition models perform in predicting these attributes? This is a crucial question because if the model cannot even recognize these attributes, we cannot reliably develop interpretable models on top of it. To investigate this, we conduct experiments to predict the 11 attributes from WBC images.
Data Split. We randomly divided the dataset into 6, 179 training images, 1,030 validation images, and 3,099 test images. The random division ensures that the cell-type distributions are the same in each set. The exact split is available in Appendix.
Model. Our baseline model predicts attribute values from image representations extracted by an image encoder of convolutional neural networks. We employ ImageNet preerrined ResNet50 as our choice of image encoder. The attribute prediction model comprises multi-task heads of per- attribute linear layers, with each layer corresponding the model simple and provide a foundation for future work, such as designing more complex model architectures or integrating domain knowledge of WBCs (c.g., cell structure). The further details about the model (including other backbones) and its training is in Appendix and the source code.
specific attribute. We intentionally keep
Evaluation Metrics. Due to the imbalance of attribute values, we macro F-measure, which calculates the harmonic mean of precision and recall, instead of plain accuracy for the evaluations. We run the same code three times with different seeds and report 95% confidence intervals.
Results. We present the results in Table [ The baseline model achieves an average macro measure of 21.20- + 0.06%. Some attributes, such granularity, granule type, and granule color, exhibit particularly high F-measures of over 98%. On the other hand, nucleus shape is the most challenging attribute predict, with the lowest F-measure of 76. + 0.59%, which could be due

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Table 1: Macro F-measure (%) for Attribute Prediction.
Cell Size 83.81 + 0.33
Cell Shape 90.66 + 0.36
Nucleus Shape 76.13 0.59
Nuclear Cytoplasmic Ratio 96.35 + 0.06
Chromatin Density 86.39 + 0.32
Cytoplasm Vacuole 89.57 + 0.47
Cytoplasm Texture 94.49 = 0.51
Cytoplasm Color 87.99 + 0.47
Granule Type 99.44 + 0.07
Granule Color 98.76 = 0.08
Granularity 99.61 0.02
(Average) 91.20 + 0.06
(a) Cytoplasm vacuole GT: Yes Pred: Yes
(b) Cell shape GT: Round Pred: Irregular
(c) Cell size GT: Big Pred: Small
Figure 4: Examples of correct and incorrect predictions with Grad-CAM. (a) Vacuoles are correctly highlighted. (b) The model incorrectly identifies leaked cellular substances as part of the cell. (c) The model overlooks the WBC and focuses on the red blood cell instead.
Manocyte
Monocyte
Neutrophil
monocyte voluminous light blue cytoplasm pierced by multiple vacuoles.
Description in Zini, et al. 2023
Cytoplasm colar: Blue Cytoplasm vacuole: Yes
Nucleus shape: Unsegrmented-indenteee Cytoplasm color: Purple blue
Granule color: Pink Granule type: Small (iii) Animal Cells
(i) Cells from COVID-19 Patient
(ii) Bone Marrow Cells
Figure 5: Examples of correct and incorrect predictions when the predictor is applied for cell images beyond our dataset: (i) Covid-19 19 Patients, (ii) Bone Marrow Cells, and (iii) Animal Cells. While not all attributes be predicted correctly, especially those sensitive staining, attribute predictor can still recognize key attributes. See Broader Applicability Sec. for further details.
to the complexity of the nucleus shape. Figure 4 highlights some prediction results along with Grad-CAM 44! visualize the areas the model focuses on. More examples are reported in Appendix.
Broader Applicability. Although we established attributes based on typical WBCs from healthy human individuals, anticipate that attribute definitions
be applied in diverse contexts. For example, Zini and O'Onofrio (29) discuss the morphological features of WBCs in COVID-19 patients, which align with the attributes we have established. To explore how well classifier can recognize them, we briefly inspected a small number of images involving cells from COVID-19 patients (29] bone marrow samples 45), and swine (a non-human species) [46]. While the attributes are still applicable, we observe that the cytoplasm color, which is sensitive to staining conditions, sometimes cannot be predicted correctly. As we can see in Figure 5 in comparison Figure 2 colors look different. Nonetheless, we find that attributes less sensitive of colors (e.g., cytoplasm vacuole) can be predicted correctly for the majority of the cases. We discuss this further in Appendix.
5 Applications
One immediate practical application is to automatically recognize morphological features from cell images, which investigated in Sec. The attribute recognition model can be incorporated into software that automatically analyzes cell morphology [28]. which provides valuable assistance in clinical diagnostics, as certain morphological features of WBCs may indicate specific diseases or conditions 47 48 It can also assist hematologists in searching for cells with specific morphological characteristies within massive datasets that would be impractical to examine manually. Beyond the cell analyzer, we demonstrate three applications that can contribute to XAI.

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image
attribute
target y
Imeqular
Round
Pink
Purple
Attribute Predictor
Coll Mucleus
Granule
Gramule
Attributes-baui based Classifier (C)
0.92
0.02
Neutrophil
Human Intervention
Attriutee- baseed
Classitter fC)
a't
0.05
Basophil
Retrieved Image that has
(a) Attributes-based classifier with interpretable intervention
(b) Counterfactual Example
Figure 6: (a) As discussed in Sec. 5.I. our dataset enables training a cell type classifier solely based on attributes. We can ask questions like, "What would be the predicted cell type if this cell had purple granules instead of pink?" (b) We can retrieve corresponding images as counterfactual examples.
Cell Type: Monocyte
Big
Cell Size
Small High
Cell Type: Lymphocyte
Low
Nuclear Cytoplasmic Ratio
Figure 7: Our dataset enables training GANs for attribute-basee image editing, which be used for synthesizing counterfactual examples. For instance, controlling cell size and NC ratio can illustrate the decision boundaries between monoeytes and lymphocytes.
5.1 Human Intervention with Highly Interpretable Models
One of our motivations in developing this dataset is to foster XAI for WBC recognition. An effective approach to enhance explainability is designing models that make predictions based exclu- sively on attributes that are easily interpretable by humans. Koh et al. [49] explored such model that initially uses CNN predict set of human-interpretable attributes and subsequently these attributes to predict the target output. Formally, the models are trained on data points of fimage x, attribute a, target y}. using predict attributes and then relying exclusively on à to estimate the target ÿ. We implemented basic version of this model using the attribute predictor developed in Sec. In particular, WBC types from the probabilities of attributes inferred from an image. Importantly, we limit the f(-) depend solely on these attribute probabilities determine the cell category. The model can predict the WBC categories with F- F-measure of 99.40 + 0.04%, whereas a CNN predicting directly from images can achieve 99.54 + 0.05%. Despite the slightly reduced accuracy, the attribute- satribute-basedd model facilitates more engaging human-model interactions, akin to Koh et al. [4), by enabling human interventions with the edited attributes ä' and observing how this impacts the prediction f(ă') versus f()). As shown in Fig. (a). we can analyze hypothetical scenarios, such what would happen if cell contained granules of a different color. Such analysis would infeasible without our dataset.
trained Ll-regularized linear softmax classifier f(-) predict
5.2 Counterfactual Example Retrieval and Synthesis
Another way of explaining classifier is to show counterfactual examples, where a slight modification the input image can result in different classification outcome by the classifier. Our attribute predictor described in Sec. can be utilized for retrieving counterfactual examples that correspond to attribute changes. Fig. (b) displays retrieved example where the pink granules of the cell in Fig. 6 (a) are changed to purple ones, illustrating the decision boundary between neutrophils and basophils. Furthermore, can even generate counterfactual examples for given WBC image by utilizing data-driven image editing techniques [50]. As
we trained an unconditional
StyleGAN (5I) and implemented GAN-based editing techniques [52 53 to modify cell size and the
nuclear cytoplasmic (NC) ratio, shown in Figure 7 These attributes are crucial in distinguishing
between monocytes and lymphocytes 31 Larger cells with lower NC ratio (depicted on
7) are likely monocytes, whereas smaller cells with higher NC ratio (shown on the right in Fig. are lymphocytes. These counterfactual examples helps us understand the decision boundaries.
left in Fig.

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Noraaized Frequency
Correctly Detected APL
Normalized Frequenc [
0.8
Correct APL ITrue Negative) Misdiagnosed APL
(True Positive)
0.6
Overloaked
(False Negative)
0.6
Positive)
0.4
0.4
Norm, Freq.
Pi Cycoolasmi
Cytoplasmy
APL (Positive)
(Negative)
0.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
P/Blue Cytoplasm)
PiBlue Cytoplasm)
P(Blue Cytoplasm)
(a) APL (Positive)
(b) Non-APL (Negative)
(c) Blased Training Set
Figure 8: (Best viewed with zoom). We analyze the binary classifier for Acute Promyelocytic Leukemia (APL) using images of promyelocytes, type of immature cell absent from our dataset. Employing the attribute predictor trained on our dataset, we estimate the probability distribution of morphological attributes in test images of (a): API. and (b): Non-API.. Notably, significant difference in P(Blue Cytoplasm) between correct predictions and incorrect predictions within (a) and (b). Specifically, plot (a) demonstrates that the classifier is likely overlook APL if the cell exhibits blue cytoplasm, whereas plot (b) indicates the classifier tends to mistakenly diagnose APL when the cell lacks blue cytoplasm. Essentially, the classifier correlates blue cytoplasm with Non-APL. This correlation is not medically supported [60] [1] and is a coincidental correlation present in the training (as demonstrated in plot (c)) of specific dataset (59) For further details, refer to Sec. 5.3
observe
5.3 Interpreting Model Bias for Blood Disorders Involving WBCs
Although our dataset annotates morphological attributes of typical cells from healthy individuals, we believe still offers significant potential for explaining the behaviors of machine learning models for atypical cells from blood disorders. Attribute predictors trained on our dataset can unveil biases that a model might have inadvertently learned, thereby improving model interpretability. Models often misclassify images that linked to particular attributes. This issue has been reported in various machine learning systems, including face recognition models that underperform on certain genders [54]. races (55), or skin colors [56]. Similar examples have been discovered in the medical field, where skin disease detection models erroneously associate malignant cases with artificial skin markers used in clinical practices [57), X-ray models misinterpreting the appearance of treatment device as pathological signs [58]. By employing our attribute predictors to identify biases, can improve model interpretability, enabling deeper understanding of decisions and ultimately contributing to XAI that assists clinicians in adapting ML models for diagnosis.
To illustrate this point, we explore the Acute Promyelocytic Leukemia (APL) detection dataset (59) derived from peripheral blood smears. This dataset presents a binary classification task recognizing APL from images of immature mycloid cells atypical category not covered in dataset. Initially, we trained a baseline classifier using ResNet50, achieving an AUC of 76.97 + 2.35%, applied our
comparable to the reported 73.9% achieved using 7-layer CNN. Subsequently, attribute predictors on the test images to examine the correlation between the classifier's performance and the attribute distributions in promyelocytes, the type of myeloid cells essential for diagnosing APL, as indicated by the P in APL, representing promyelocytic, the adjective form of promyelocyte.
We explored the attribute distribution and the classifier's performance in actual APL cases and actual non-APL cases. closer inspection of all attributes, we found that promyelocytes characterized by blue cytoplasm were frequently misclassified as false negatives, illustrated in Fig. (a). Examining the training set more thoroughly to locate the source of this bias, we found a clear correlation between blue cytoplasm and non-APL cases (Fig, (c)). To validate this bias, consulted relevant literature [60] 61 and inspected actual images within this dataset. We concluded that this was a spurious correlation present in the training set, specific to this dataset [59]. This example clearly illustrates how our dataset can help in identifying model biases, thus promoting model interpretability for XAI in medical diagnostics.

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Conclusion
We have presented a densely-annotated dataset for WBC recognition, containing 11 morphological attributes for 10,298 cell images. This dataset addresses the current gap in the development of explainable and interpretable machine learning models for WBC analysis, a crucial task in hematology and pathology. We trained an automatic attribute recognition model and showcased several specific applications that can be developed using our attribute annotations. We hope that our dataset will foster advancements in XAI in the fields of pathology and hematology.
Limitation. We annotated images from single source [6], which is limited to typical cells and does not include abnormal cells from blood disorders, which may be of greater clinical interest. Annotating these cells a future direction. The cytoplasm color in our attribute definitions assume the use the May Grünwald- Giemma-Giamaaaa staining method, and they may appear differently with other staining methods (e.g., the color purple in granules may not look purplish). However, domain adaptation could mitigate these distributional shifts. Creating a similar dataset with other staining methods is future work.
10

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Acknowledgments and Disclosure of Funding
This project is supported by the Sysmex Corporation. We thank Mari Kono and Joao Nunes for helpful discussion. The research was carried out at the ROSE Lab at the Nanyang Technological University, Singapore. We thank Siyuan Yang. Takuma Yagi, and Lalithkumar Seenivasan for their valuable feedback on the manuscript. We also thank anonymous reviewers for their constructive suggestions. The computational work for this article was partially performed on resources of the National Supercomputing Centre (NSCC), Singapore (NSCC project ID: 12003626).
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7 Appendix
7.1 Broader Impacts
Our study was exempt from institutional IRB approval as we added labels to publicly available data. We do foresee immediate ethical concerns in our dataset as we annotate well-anonymized public data and rely on medical literature for attribute definitions. However, we acknowledge that every dataset inevitably influenced by the potentially biased values of its creators. While the bias may be less significant in white blood cells compared to domains such as aesthetics [22), face [54], or skin 56
we remain committed to addressing any ethical concerns that may arise future.
7.2
Dataset Files
Our dataset consists of three csv files, which are hosted at htttt:// com/ apppe23pp/ #bccat/tree/matt htt.t/eeeeaaanuumieesssseesseees suann/submission Moreover, the files are small enough be permanently hosted on the NeurlPS website as supplementary material. This ensures long-term accessibil- ity and backup. The three files of pbc_pttr_s___ siar__l_trinnn cav, stt_aattt____eee _e__attr_vv_teee cav contain attribute annotations for the train/val/test splits.
and
cell_shae cell_shape,
nucleus_shape
nuclear_ cellear_cytoplammc__ee pratia,
chromatin chromatii_densitty. cytoplasm_ vacuole, cytoplasm_ textureeeeeee,, cytoplasm colour, granule_type, granule_colouee and granularity. Attribute columns. We annotated them, which is the main contribution of this paper.
label: One of the five WBC types (neutrophils, eosinophils, basophils, monocytes, and lympho- eytes) provided by the PBC dataset.
name: This is the image file name. It can serve as a unique identifier.
path: Image path organized by the PBC dataset.
For images, please download from the PBC dataset: https://data. mendeley. mendee//ll.a...eeeee...... http://aat..eeeeeee...ocoaaaaaas www.datasetaaa.a snkd93bnjr/1
7.3
License
Our dataset is under the MIT license.
7.4
Details of Attribute Prediction in Section 4
7.4.1
Model Details
Let M denote the attribute prediction model, / for the input image, and A for the predicted attributes. The model M consists of an image encoder E and an attribute predictor P.
The image encoder E' (for example, ResNet) processes the input image I and generates the feature vector F::
== E(T)
The attribute predictor P is comprised of several attribute predictors P each being a fully- connected layer tasked with predicting the i-th attribute. Each attribute predictor P. accepts the image features F as input and outputs the predicted distribution A, for each attribute:
A; = PP(F)
The predicted attributes A collate all the predicted attribute distributions A serving the predicted attributes for the input image /:
= [A]
The overall prediction of the attribute prediction model M for the input image can be expressed as:
= M(1) P (E()),
15

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Pa
Ao
:
0.1 Pink
Image Encoder E.g., ResNet
Pt
A
0.2 Purple Red Attribute A;
Image Feat.
PR
An
E.g. Pt for granule-color
Fully-connected layers {Pi)== for attributes [ADi=)
The architecture of the attribute prediction model. The model accepts input image, which is processed by an image encoder E to yield a feature vector. This vector is then processed by multi-task attribute predictors {P.) 1
Figure 9:
to produce predicted attribute distributions {A:)-=
where P2.P........ Pa] represents the composition the attribute predictors.
The architecture of this model is illustrated in Figure Training this network inherently involves multi-task learning, where each task is to predict specific attribute. The model takes an input image, which is processed by shared image encoder to yield a feature vector. This feature vector is then utilized by several attribute predictors Pi each responsible for generating predicted attribute distributions A.z-
7.4.2 Implementation Details
use the Adam optimizer with a learning rate 0.0001, weight decay of 0.01, and a batch size of 128. We train the model for 30 epochs, selecting the best-performing model based the validation set, and evaluate its performance using the test set. The PyTTorch code for this model available in attribute, ceteeeeppeiittoo py, which contains the implementation of the AttributePredicton class. The code to execute the experiment is traineval.
7.4.3 More Baselines
The main paper presents results utilizing the backbone of ResNet50 62 which is of the most frequently used image classifiers and has been reported as having received the highest number of citations in the field of artificial intelligence [63]. As mentioned in the main paper, have intentionally kept the model simple. We believe that reporting results with this widely used backbone is beneficial it provides solid foundation for future work. However, in Table 6 also have results from other backbones such as VGG16 (64), ViT-Base/16 [65]] and Conv [66].
7.5 Annotation Process and Quality Control
We used Label Studio 67 (see Figure 10) as the annotation tool. To ensure the accuracy and reliability of the annotations in our dataset, we implemented a comprehensive quality control process. This involved multiple steps and the participation of domain experts. The following sections outline the key aspects of our annotation quality control.
(i) Recruiting Qualified Annotators. We called for students majoring in biomedical sciences who claimed have basie knowledge of WBCs. We requested each of them to provide a short description of the five types of WBCs to ensure that they possessed the required foundational knowledge. We invited those who provided satisfactory answers to an information session, where we clearly explained the morphological attributes using materials similar to those presented in the main paper (Sec. 3.1 We then recruited them perform a pilot annotation 100 images (see next).
Sec. 3.4 and Figure 2
(ii) Pilot Annotation. After the aforementioned screening process, we instructed each candidate to independently annotate randomly selected subset of the same 100 images. We intentionally used the same images to evaluate their understanding and enable comparisons among the candidates. We acknowledge that the candidates annotated the images while being aware of the cell type. This was necessary compromise we made by involving medical students instead of hiring pathologists due to cost limitations. The cell types the PBC dataset had already been verified by pathologists, and this knowledge aided the students in producing higher-quality annotations. For instance, they could
16

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Nucleus Shape
Granulerity
Segneetied-uuttee
Unsegmented varrcl
nddereeff
Imogulzee
Granule Type
26
Chromatin Density
Small
Round
Coarself
MM
Loosely
Denseyet
Call Size
Nucles--cytoplammic Ratio
Smadth
tight
High low
Cell
Cytoplasm Colour
Inssulaf
Light
Unoxpected
Cytoplasm Texture
Cleam
Frosted
Cytopleam Vacuole
Veei
Nos
Figure 10:
The screenshot of Label Studio, the labeling tool used to annotate the WBCAtt dataset. Annotators can determine the cell type from the name associated with the cell, which is provided before image selection. The image to be annotated is presented in a clear and organized manner, enabling annotators easily navigate and zoom in the image. Attribute selections organized using radio buttons, ensuring that only one choice can be selected at a time. All attributes must be selected before submitting and proceeding to the next image.
utilize the cell type as prior knowledge, such as understanding that lymphocytes from healthy patients should not possess multi-lobod nucleus.
(iii) Annotation Phase and Feedback. Based on their performance on the pilot annotation, we selected the students and assigned them 200 images for what we refer to as Phase 1. In this phase, each student annotated a unique set of images, with allocation decisions taking into account their individual strengths. For instance, if student demonstrated superior proficiency in annotating neutrophils compared to other cell types, we allocated a higher number neutrophil images to that student. Subsequently, conducted thorough review of the annotations, providing specific feedback and updating our attribute descriptions to improve clarity.
(iv) Annotation Phase with Regular Discussions. After completing Phase 1, we allocated the remaining images to the annotators and encouraged them to report any uncertain cases for further discussion. In cases of ambiguity, we discussed with the pathologists who defined the attributes with us, ultimately reaching a consensus. These discussions and consensus-building efforts were instrumental in ensuring the consistency and high-quality of the annotations.
(v) Review and Validation. Upon completion of Phase 2, our research scientists, who were not directly involved in the annotation process, meticulously reviewed and validated all annotations for correctness and adherence to the attribute definition. This means that each image in our dataset is reviewed at least by two individuals. We corrected eтrоrs or inconsistencies through this refinement process, thereby enhancing the quality and accuracy of the annotations.
(vi) Reliability Analysis. To assess the reliability of our annotations, we randomly selected a subset of 1,000 images, and replicated annotation process with different annotators. Out of the 11,000 (= 11 1,000) attribute annotations, 10,569 were consistent with the original annotations, resulting in an agreement rate of approximately 9%.1.. Details of the per-cell agreement rate and the per-attribute agreement rate are also tabulated in Figure []] This high agreement demonstrates the reliability and robustness of the annotations.
Case Studies Attribute Prediction
As mentioned the main paper, we examine specific prediction results, both correct and incorrect ones, produced by our attribute prediction model. We use Grad-CAM to highlight the areas the model
17

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1.000
Neutrophil-
0.935
0.940
1.000
1.000
1.000
1.000
0.985
0.995
0.935
0.990
0.985
0.975
Eosinophil
0.980
1.000
1.000
1.000
0.995
1.000
1.000
0.975
0,950
WBC Call types
0.925
Basophil-
0.964
Lymphocyte
0.980
0.945
0.865
1.000
0.860
1.000
1.000
1.000
0.930
0.875
Monocyte
0.930
0.980
0.950
0.880
0.885
0.980
0.935
1.000
1.000
1.000
0.950
0.850
Average
0.894
0.959
0.896
0.958
0.976
0.948
0.987
0.999
0.987
0.997
0.961
0.825
Nucleus Shape
Chromatth Densky
Cytoplasm
Gytopibmmrrnnuittt
Granule Color
Granule type
Average
0.800
Siee
Shape
Ralio
Color
walure
Vacucle
Attributes
Figure 11: The inter-annotator agreement random sample of 1000 images from the WBCAtt dataset. Approximately 96. %% of the annotations showed consistency between the original annotations and the re-annotations by different annotators. The last column represents the per-cell agreement rate, while the last row corresponds to the per-attribute agreement rate.
focuses on. In this appendix, we present additional cases that were not included in the main paper due to space limitations.
7.6.1 Successful Cases on Our Dataset
When the model correctly predicted attributes, we expect that the Grad-CAM heatmap will emphasize the cell structure in line with the attribute definition. For instance, if the model is predicting the nucleus shape, it should highlight the nucleus prominently. Figure shows two examples of successful attribute predictions with Grad-CAM heatmaps. In these instances, the model effectively localizes the attributes. The Grad-CAM heatmaps focus on the cell edges when predicting cell shape and cell size, as shown in Figure (b). (c), (i), and (j). At times, the localization is extremely precise. Particularly, for the nucleus-shape prediction in Figure (d), the Grad-CAM heatmap accurately marks the thin filament of the nucleus, which serves as key determinant for segmented nucleus identification. Moreover, the Grad-CAM can localize the cytoplasm-vacuu
as shown in Figure (m), and highlight the cytoplasmic area during the prediction of cytoplasm color, as depicted Figure (n).
7.6.2 Failure Cases on Our Dataset
Subsequently, we investigate the cases where the model incorrectly predicted the attributes. We expect the Grad-CAM heatmaps to indicate the reasons behind the model's 's failures. After manually examining multiple instances of failed predictions, we have identified the following potential factors contributing to incorrect predictions:
(i) Presence of Two Cells in Image. The existence of multiple distinct cell types within image can present a challenge for the attribute predictor. Given that attributes are highly specific to different cell types, this situation can cause confusion during attribute prediction. For example, in Figure (i),,,, both a neutrophil and a lymphocyte are present in the same image. The lymphocyte considered the ground truth for the cell type in this image, as annotated by the creator the PBC dataset (not by us), primarily because the lymphocyte occupies a more central position. Consequently. our attribute annotations are based on the lymphocyte rather than the neutrophil. During attribute prediction, even though the Grad-CAM heatmaps focus on the lymphocyte when predicting cell-shape (Figure 13 (i) and cell-size (Figure (i)(),, the attribute predictor looks at the neutrophil when predicting other attributes such as granularity (Figure (3)())))) granule color (Figure (i)()) and granule type (Figure (i))). These attributes related granules are typically not triggered for lymphocytes in most cases, resulting in incorrect predictions.
18

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[a]
(b) Cell-shape
(c) Cell-size
(d) Nucleus-shape
(e)
(f) Granule-color
(s) Granule-type
Neutrophil
Granularity
GT: Round Pred: Round
GT: Big Pred:
GT: Segmented-
GT: Yes
GT: Pink
GT: Small
Pred: Segmented BeBd
Pred: Yes
Pred: Pink
Pred: Small
(h) Monocyte
(i) Cell-shape
(i) Cell-size
(k) Nucleus-shape
(1) NC-ratio
(m)
(n) Cytoplasm-color
Cytoplasm-vacuole
GT: Irregular Pred: Irregular
GT: Big Pred:
GT: Irregular Pred: Irregular
GT: Low Pred: Low
GT: Yes Pred:
GT: Purple Blue Pred: Purple Blue
heatmaps of accurate attribute predictions for neutrophil ((a)-(g) and a monocyte ((h)-nn)). Among the 11 attributes, we have selected six attributes that effectively differentiate between the respective cell types (neutrophil and monocyte).
Figure 12:
(ii) Broken Cell vs (iii) Leaked Cellular Substances. Sometimes the structure of cell is broken. Figure 13 (ii) shows an broken cell where the cell membrane unrecognizable, thus making the cell-shape irregular, which is correctly predicted. On the other hand, Figure (iii) illustrates an oval- shaped basophil surrounded by some cellular substances. Despite the presence of these substances, since its cell membrane remains clearly distinguishable, we annotated as having round cell shape. However, the Grad-CAM heatmap reveals that the substances outside the cell are interpreted as cell boundary, leading to incorrect prediction of an irregular cell shape for this basophil.
(iv) Overlooking WBC. We have observed instances where the WBC is overlooked. and the Grad- CAM heatmap highlights the red blood cell (RBC) instead, as depicted in Figure (iv). Con- sequently, this leads incorrect predictions, particularly when estimating cell size since RBCs generally have smaller sizes compared to WBCs.
7.7 Towards Broader Applicability
We established the attributes by analyzing five major types of WBCs derived from peripheral blood samples of healthy human individuals. Nevertheless, our attribute definitions are applicable in broader contexts. Moreover, anticipate that the attribute predictor trained generalizability to other domains the majority cases. However, formally exploring this aspect requires constructing datasets and conducting rigorous evaluations, which constitute future work. As a preliminary step, we conducted small-scale case studies to evaluate the applicability of the attribute predictor to cell images beyond our dataset. Specifically,
dataset will demonstrate
manually examined small number of cell images of (i) peripheral blood samples from COVID-19 patients 29), (ii) bone marrow instead of peripheral blood 455, well (iii) peripheral blood samples from non-human species, namely juvenile Visayan warty pigs [6)]
(i) Cells from COVID-19 Patients. As shown in Figure 14)(i), the majority of predictions made by our model, which was trained on healthy peripheral blood cells, are consistent with the descriptions provided in [29] (listed at the bottom of respective image, in italic). However, there one exception, as the prediction related to cytoplasm color in Figure (i) was inaccurately determined. This discrepancy could be attributed to the difference in staining compared that of our training data.
(ii) Cells from Bone Marrow. As illustrated Figure (ii,, our attribute predictor successfully identified the attributes that related to the cytoplasm and granules, showcasing its effectiveness. in investigating blood cells bone marrow samples. However, incorrect predictions were observed for cell and nucleus-related attributes, such cell size and nucleus shape. The poor prediction
19

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performance in cell size may be due to the nature of the bone marrow dataset, which crops the WBCs higher magnification level. (To observe the difference magnification levels, compare the red blood cells in Figure (i) to Figure (i) and Figure [4 (iii)) Since we define the size of a cell based on its relative size compared the red blood cells, all WBCs Figure (ii) are small cells, Two cells in image
(a] Lymphocyte
(c) Cell-size
(d) Nucleus-shape
(f) Granule-color
Cell-shape
Granularity
Granule-type
GT: Round Pred: Round
GT: Small
GT: Unsegmented-Round
GT:
Nil Pred: Small
Pred: Small
Pred: Segmented-Bilenn BiBllbbed
Pred: Yes
Pred: Pink
Broken Cell
(iii) Leaked Cellular Substances
Overlooking WBC
(a) Eosinophil
(b) Cell-shape
(a)
(b) Cell-shape
(a) Eosinophil
(b) Cell-size
Basophil
GT: Irregular
GT: Round Pred: Iiregulaar
GT: Big Pred: Small
Pred: Imregular
Figure 13: Potential factors contributing to incorrect attribute predictions. Please refer to Appendix 7.6 for more detailed descriptions. Correct predictions are highlighted in green, while incorrect predictions highlighted in red.
even though they occupy larger number of pixels in the image. This could also mean that our model did not actually learn the size the way we defined it; it may simply checking the absolute size in the image rather than checking the size relative to the red blood cells. Exploring this further is future work.
(iii) Cells from Pigs. To investigate the applicability beyond human blood samples, we explored animal blood smears, as shown in Figure (iii). We observe that their staining and smear preparation methods, which are different from the PBC dataset, have a substantial impact on the prediction performance, particularly for color-related attributes. In Figure [4] (iii)(),, for instance, the model fails to predict the presence of eosinophil granules due to significant differences compared to cosinophils in other datasets. (Please refer to Figure (i))c)) and Figure [3] (iv)(a) for the granule color of cosinophils in the bone marrow dataset and the PBC dataset, respectively.) Morcover, the nucleus and granules of Figure (iii)a) hardly recognizable, even human observers. This difficulty in recognition leads to incorrect predictions for nucleus shape.
7.8 Details Attribute-based WBC Classifier Section 5.1
We simply use the predicted probability (ranging from 0 to 1) of each attribute value to predict the cell types. This (lazy) formulation can cause the multicollinearity problem, so we employ the LI regularizer. The train/val/test split is the same as dataset split. The specific linear classi- fier we use is sklearn. linear model. SCDClassifier feax_iter=1000, Loss="log_los"" penalty="11") in scikit-lear!"
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Cell Size
Big
Cytoplasm Texture Clear
Cell Shape
Irregular
Cytoplasm Color Bluu
Nucleus Shape
Unsegmented
Cytoplasm Vacuole Yes
indented
Granularity
Ratio
Low
Granule Color
(a) Monacyte Chromatin Density Densely
Granule Type Nil
(0) COVID-19
monocyte with voluminous light blue cytoplasm pierced by multiple vacuoles.
The nucleus is C-shoped.
Patients
Cell Size
Big
Cytoplasm Texture
:Clear
Cell Shape
Irregular
Cytoplasm Color
:Light blue
Nucleus Shape
Segmented
Cytoplasm Vacuole No
bilobed
Granularity
NC Ratio
Low
Granule Color Pink
(b) Neutrophil
Chromatin Density Densely
Granule Type Small
A neutrophil with a bilobed nucleus, wwwpddo-eelgereeeeeee Pelger-Huët type.
Cell Size
Big
Cytoplasm Texture :Clear
Cell Shape Nucleus Shape
Irregular
Cytoplasm Color Cytoplasm Vacuole No
:ight blue
Segmented
bilobed
Granularity
:Yes
NC Ratio Chromatin Density
Low
Granule Color
Pink
(a) Neutrophil
Densely
Granule Type :Small
Cell Size
Small
Cytoplasm Texture
Frosted
Cell Shape
Irregular
Cytoplasm Color
BBlue
(ii)
Nucleus Shape
Unsegmented
Cytoplasm Vacuole
Bone
indented
Granularity
No
Marrow
NC Ratio Chromatin Density
High
Granule Color Nil
[b) Lymphocyte
Loosely
Granule Type Nil
Cell Size
Big
Cytoplasm Texture :llear
Cell Shape
Round
Cytoplasm Color
Lighh blue
Nucleus Shape
Segmented
Cytoplasm Vacuole No
bilobed
Granularity
YYes
NC Ratio Chromatin Density
Low
Granule Color : Red
Eosinophil
Densely
Granule Type Round
Cell Size
Big
Cytoplasm Texture Clear
Cell Shape
Round
Cytoplasm Color
Lighh blue
Nucleus Shape
Segmented
Cytoplasm Vacuole No
multilabed
Granularity
YYes
NC Ratio Chromatin Density
Low
Granule Color Red
Basophil
Densely
Granule Type
Coarse
Cell
Big
Cytoplasm Texture Frosted
Cell Shape
Round
Cytoplasm Color : :Purple blue
(iii)
Nucleus Shape
Segmented
Cytoplasm Vacuole No
Animal
bilobed
Granularity No
Ratio
Granule Color Nil
(b) Eosinophil
Chromatin Density
Loosely
Granule Type Nil
Cell Size
Small
Cytoplasm Texture Cleaa
Cell Shape
Round
Cytoplasm Color Light blue
Nucleus Shape
Unsegmented
Cytoplasm Vacuole No
round
Granularity
:o
NC Ratio Chromatin Density
Low
Granule Color Nil
(c) Lymphocyte
Densely
Granule Type Nil
Figure 14: The prediction results of attribute predictor were evaluated for characterizing cells
obtained from: (i) peripheral blood smears of patients with COVID-19, (ii) bone marrow smears, and (iii) peripheral blood smears of animal (juvenile Visayan warty pigs). The descriptions of (i)(a)
and (i)(b) were summarized from [29]. Correct predictions are highlighted in green, while incorrect predictions highlighted in red.
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7.9 Details of StyleGAN-basedd Image Editing in Section 5.2
We trained StyleGAN-v [1 and subsequently pixel2style2pixel (pSpl encoder. These models enable us to embed cell images into the latent space of the trained StyleGAN, allowing for GAN inversion. Theoretically, this approach allows us to embed any cell image into the GAN latent space. By manipulating the latent space, we were able to edit the generated images, which is shown in the main paper. Specifically, we explored two techniques: GANSpace [52] which applies Principal Components Analysis (PCA) to the latent space, and StyleSpace [33, which can identify the subspace of the GAN latent space corresponding to certain attributes based on example images. Using these methods, discovered that specific principal component can control the cell size and NC ratio, as depicted in Figure 7 in the main paper. Additionally, although not presented in the main paper, found subspaces to edit nucleus shape (Figure 13 (a)) and cytoplasm texture (Figure [5 (b)). We acknowledge that we did not rigorously evaluate controllability and image quality of these two techniques, as primary aim to showease the usability of our attribute dataset. The code used for these experiments was obtained from the authors of the referenced papers (see the corresponding footnotes).
[5
Band
Bilobed
Clear (b) Cytoplasm texture
Forested
(a) Nucleus shape
Figure 15:
Applying StyleSpace 53 on our attribute annotation, we discovered the way to control
the necleus shape and cytoplasm-texture.
7.10 Details of APL Detection in Section 5.3
We used the images provided by the dataset [59] and followed their proposed data split except that we removed the duplicated and corrupted images that we identified. We publicly reported the duplicates and the corruptions ? but we have not received any comments thus far. Once ResNer50 was trained to classify APL or non,APL, we applied our attribute predictors to the promyelocyte images in the test set. Subsequently, we visualized the probability distribution of each attribute for the four groups (true positive, false negative, true negative, and false positive). Upon manual investigation of all attributes, discovered significant bias in the blue cytoplasm, illustrated in Figure (b) in the main paper. is worth noting that the inherent morphological differrnnces between APL and non-APL promyelocytes may lead to distinct attribute distributions. In other words, if attribute is crucial feature for diagnosing APL, the bias is not necessarily undesirable. However, in this specific case, we found that the correlation only existed in the training set, and could not find any literature supporting this bias. Consequently, we concluded that the correlation was spurious.
7.11 Attribute-based Acute Lymphocytic Leukemia Prediction
Our dataset provides annotations solely for images of healthy patients. However, anticipate that the attributes we've identified can be exploited for downstream tasks with direct clinical implications, such certain disorders. In this context, detection of Acute Lymphocytic Leukemia (ALL), which is a significant challenge in the field of pathology. We employ the ALL-IDB 4 dataset, specifically the ALL-IDB2 subset, which includes cropped cell images. We follow the train/val/test split and baselines reported by Genovese et al. (69 who are from the same research group that developed the ALL-IDB dataset.
investigate the potential use of these attributes for the
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Table 2: Results for Predicting Acute Lymphocytic Leukemia (ALL)) on the ALL--DBB 4
Method
Accuracy (%)
VGGI6
87.54 + 3.15
69
ResNet18
88.69 + 2.67
69
ResNet18 Pre-trained on Additional Histopathological Images
97.92 1.62
ViT-Base
91.28 + 2.17
Logistic Regression Predicted Attributes Gradient Boosting on Predicted Attributes
81.03 = 1.48
83.85 + 1.88
Logistic Regression Attributes Predicted with Domain Adaptation
86.92 = 2.13
Gradient Boosting on Attributes Predicted with Domain Adaptation
90.77 + 1.23
Due to the lack of attribute annotations for the ALL.IDB dataset, we employ our model, trained on our dataset, to predict attributes. We utilize the ViT-Base backbone for this task, as our preliminary examination of a small number of cases indicates that the predicted attributes are more accurate than those from other backbones. This observation aligns with previous research (70), which suggests that ViTs typically yield higher cross-dataset accuracy than CNNs. We also implement a straightforward domain adaptation technique to concurrently minimize the classification loss and the Maximum Mean Discrepancy (MMD) [7]] loss between the labeled domain (our dataset) and the unlabeled test domain (the ALL-IDB dataset).
After obtaining the predicted attributes on ALL-IDB images, we trained two types of binary classifiers for ALL detection: L. L.I-regularized multiclass logistic regression aarressioraandd and a gradient boosting classifie This is done using the predicted attributes with and without domain adaptation. We ran the code three times with three different seeds and reported the mean and 95% confidence intervals. We compared these results with the image-based classifiers using ViT-Base, as well as ResNet18 and VGG16 (the two baselines reported in [69]]
Table 2 summarizes the results. The accuracies of attribute-baase prediction methods are comparable to those of basee-based prediction methods, suggesting the feasibility of using our attributes as interpretable features for clinical downstream tasks.
7.12 Peer Loss for Potential Annotation Noise
In response to suggestion from reviewer, we have explored the peer loss (72.. which is robust against label noise and does not require to specify the noise rate. We conducted the following experiments using the ResNet50 backbone and executed the code three times to obtain the mean and the 95% confidence intervals.
Table Impact of Peer Loss on Average Macro Fl Score
Dataset
Utilize Peer Loss?
Average Macro F1 Score (%)
Training Set with 10% Label Noise Training Set with 10% Label Noise
No
85.31 + 0.24
Yes
88.86 + 0.02
Training Set without Added Label Noise Training Set without Added Label Noise
No
91.20 + 0.06
Yes
91.17 = 0.03
To assess the efficacy of the peer loss on our dataset, we intentionally introduced noise by perturbing 10% of the annotations within the training data. Specifically, from the pool of 67, 67,969 categorical values (6, 179 images x 11 attributes), we randomly selected 6,797 values (10%) and replaced each with an alternative value chosen uniformly from the remaining possibilities. Using this perturbed data, we trained our model both with and without incorporating the peer loss. The outcomes, presented
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in the upper section of Table 3 reveal that training on the noisy data alone yields an average macro Fl score of 85.31 0.24. However. integrating the peer loss increases this score 88.86 0.02. demonstrating the efficacy of the peer loss.
After confirming the effectiveness of peer loss, applied it to our training data without any artificially introduced noise. As shown in the lower part of Table utilizing peer loss yields an average macro Fl score of 91.17 + 0.03, while training without results in score of 91.20 0.06. The nearly identical scores indicate that the impact of peer loss is not significant, suggesting that the presence of annotation noise within our dataset limited.
7.13
Other Tables and Figures
Table Frequencies of attribute values, illustrating content similar to Figure in the main paper.
Table
Initial attributes used as starting point to differentiate the five types of WBCs.
Table
Precision, recall, and F-measure values for each attribute value.
Figure 16
GradCAM heatmaps for different seeds and backbones that are not reported in the main paper.
Figure 17 Visualization of how staining conditions and imaging equipment influence the attributes.
Table 4: Attribute Dist. The distribution represents the results of annotating all typical WBCs from the PBC dataset, which is the image source we utilized. We did not actively control or manipulate the distribution.
Attribute
Value (Count)
Cell-Size
Big (4,997), Small (4,271)
Cell-Shape
Round (7,173), Irregular (2,095)
Mucleus-Shape
Segmented-Binn -Bilooeddd- (2,806),
Unsegmented-Be -andd (2,356),
Unsegmentedd-Ineenee (1,205), Segmented-Multiloddee (1,143),
Unsegmented-Ron Rounnd (967), Irregular (791)
Low (8.148 (48), High (1,120)
Ciromatin-Density
Densely (8,44) (8,443), Loosely (825)
Pttoplasm-Vacuoll
No (8,559), Yes (709)
Cytoplasm-Texxie
Clear (7 (,,429) Frosted (1,839)
Cytoplasm-CC -Cllor
Light Blue (7,011), Blue (1.273), Purple Blue (984)
Granule- Type
Small (3, (3,003), Round (2,801), Nil (2,374), Coarse (1,090)
Granule-Color
Pink (2,925), Red (2,803), Nil (2.373), Purple (1,167)
Granularity
Yes (6,896), No (2,372)
Table 5: Coarse Morphological Attributes
Cell Type
Nucleus Structure
NC Ratio Granularity
Granularity Color
Cell Size
Basophils Eosinophils
Segmented
Low
Yes
Blue / Black (dense) Red
Segmented
Low
Yes
Lymphocytes
Unsegmented
High
No
Small
Monocytes
Unsegmented
Low
No
Neutrophils
Segmented
Low
Yes
Blue
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Table 6: Macro Precision (Pre.), Macro Recall (Rec.), and Macro F-measure (F-m.) of VGGI6, ResNet50, ViT-Base, and ConvNeXI- Tiny (CNXT-T) for the task of attribute prediction.
Cell Size
Cytoplas
Cyoplim
Cyiopham
Granularity (Average)
Shape
Shape
Cyto- plasmric Ratio
Density
Vacuole
Texture
Color
Type
Color
VGG16
Pie.
+0.61
+1.80
+1:26
+-0.8
+0047
+0.10
+0.31
VOGI6
83.44
90.13
74.18
05.12
86.
85.96
95.21
83.03
98.42
96. 06.54
99 09.90
90.20
Rec.
1.0.42
100.07
10.50
10.99
12.56
1.1.39
11.35
10.95
1.0.12
1.0.1
10.05
20.70
VGGi
83.44
F-m.
1.0.5
ResNer50
84.21
90.73
77.08
97.47
94.05
51: 71
90.22
88.24
96.36
98.89
00
91.46
Pre.
210661
1:000
17.47
10.118
1:0.48
1:1.76
10.87
1.0.0
20.30
2:0.8
2.001
20.30
ReaNet50
$2.69
90.64
75.70
96,30
98,52
$7.06
88.06
96.52
63
62
+00.44
+0.50
ResNer50
60.81
90.08
76.13
96.35
86.30
59.57
94.49
87.99
98:44
98.76
99.61
91.20
F-m.
+1.33
+0.36
+0.59
+0.06
+0.32
+0.47
+0.51
+0.47
+0.07
+0008
+0.02
=0.06
VIFB
53.58
59.53
77.21
96.87
64.73
90, 62
92.86
87.62
99.32
99.16
61
91,01
Pre.
+:014
+0.64
+0.27
+:2.44
+0.97
=0.06
=0.02
=0.30
VIT-B
83.01
75.88
95
84.61
96.16
88.01
99:45
98.
66
91.02
Rec.
=:0.52
+0.52
12.00
#:0.76
422.90
10.70
10.96
++0000
200.10
20.19
20.05
20.32
VIT-B
85.19
$0.80
75.94
96.37
84.51
90.63
93.94
87.70
99.39
65
90.91
Fm
10.40
10.51
11.24
1.0.29
41.78
10.19
10.38
10.45
10.11
10.11
1.002
CNXTT
83.50
85.56
Pro.
100.43
10.61
11:27
1.0.42
1.0.68
1..61
10.34
1.0.19
1.0.09
1.0.16
1.006
1..25
CNXTT
83.44
91.54
77.68
95.665
86.87
$8.04
95.00
88-28
99.64
98. 75
98.71
91.24
Roc.
1:0.35
1:0.39
1.1.8
1:0.22
1:0.85
1.0.26
110777
1.0.77
1.0.01
1:0.08
1:0.05
2.026
CNXTT
2:037
+00.0
#1.56
+40.4
1-0.1
+-0.0
+0.27
1-0.46
1:0.06
2:0.2
+0004
ResNet50, Seed
ResNet50, Seed 100
VGG16, Seed 0
ConvNeXt-Tiny, Seed
VIT-Base, Seed
(a) Cytoplasm vacuole
GT: Yes
Pred: Yes
Pred:
Pred: Yes
Pred: Yes
Pred: Yes
(b) Cell shape
GT: Round
Pred: Irregular
Pred: Irregular
Pred: Inregular
Pred: Irregular
Pred: Irregular
Cell size
GT: Big
Pred: Small
Pred: Small
Pred: Small
Pred: Small
Pred: Small
Figure 16: Grad-CAM heatmaps from different seeds (ResNet50 with seed 100) and various back- bones (including VGG16, ConvNeXt-Timg -i-yy, and Vit-Base), while images are the same as Figure 4
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Granule Color Pink
Cytoplasm Color Blue
Red
Purple
Light Blue
Purple Blue
PBC Dataset
APL Dataset
Raabin-WBC
Figure 17: WBC appearances under different staining and microscopy conditions. WBCs sourced from the PBC Dataset [6) (used in this work) have undergone May Seenwald-Giemss staining and were acquired through the automated digital cell morphology analyzer, CellaVision DM96. WBCs from the APL Dataset [59] are stained using Wright's method and observed through the CellaVision DM100 cell analyzer. On the other hand, the Raabin-Wai 7 images, stained with Giemsa, are captured using smartphones mounted on both Olympus CX18 18 and Zeiss microscopes.
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Table 7: Precision, Rocall, and F-measure of VGG16, ResNer50, VIT-Base, and ConvNeXi-Tiny (CNXT- Each Altrbbute Value
Backbone
Autibute Name
Attribute Value
Precision
Recall
F-measure
VGG16
Cell
Big
85.17 + 0.32
= 1.65
85.06 + 0.67
VGG16
Small
4:
+ 0.80
1
VGG16
Shape
Trregular
82.37 +: 4.34
+ 2.99
83.89
VGG16
Shape
Round
0.77
VGG16
Nucleus Shupe
Irregular
2.93
1 7.88
VGG16
Nucleus Shape
Segmeuted-Biine Biilobed
74.98
0.73
74.70
VGG16
Nucleus Shape
Sggmented-Muttilnn
VGG16
Nucleus Shape
Unsegmented Band
80.65
88.86 0.42
84.55 0.29
VGGI6
Nacleus Shape
Unsegmented- Indentedd
81.87
76.22
78.91 1.55
VGG16
Nucleus Shupe
Unsegmented Round
83.31 3.21
52.19 5.07
82.60 1.51
VGG16
NC Ratio
High
94.80 2.25
90.93 + 2.15
92.79 1 0.61
VGGI6
NC Ratio
Low
98.76 0.29
99.30 = U.32
99.03 + 0.09
VGG16
Chromatin Densily
Densely
97.61 0.57
96.66 + 0.68
97.13 1 0.12
VGGI6
Chromatin Deasity
Loosely
70.34 2.98
76.77 5.76
73.23 + 1.45
VGG16
Cytoplasm Vacuole
No
97.72 +: 0.24
98.87 + 0.20
98.29 1 0.07
VGG16
Cytoplasm Vacuole
Yes
84.77 +: 1.83
73.06 = 2.95
78.43 + 1.20
VGG16
Cytoplasm Texture
Clear
98.52 0.91
95.99 + 0.75
97.23 1 0.07
VGG16
Cyioplasm Texture
Frusted
86.05 1.84
94.43 1 3.51
89.98 1 0.63
VGG16
Cytoplasm Color
Blue
77. 3.15
78.83 + 0.60
VGG16
Cytoplasm Color
Light Blue
98.90 +
99.11 = 0.26
99.00 + 0.20
VGGI6
Cytoplasm Color
Purple Blue
73.27 +: 2.98
75.20 = 6.06
74.01 + 1.30
VGG16
Granule Type
Coarse
98.76 + 0.15
99.52 = 0.31
99.14 + 0.13
VGG16
Granule Type
Nil
99.22 1 0.07
99.51 1 0.11
99.36 1 0.09
VGG16
Granle Type
Round
99.75 +: 0.06
99.46 + 0.30
99.60 1 0.13
VGG16
Granule Type
Small
99.41 + 0.33
99.18 + U.11
99.29 + 0.16
VGG16
Granule Color
99.18 = 0.07
99.63 = 0.00
99.40
VGGI6
Granule Color
Pink
98.46 = 0.40
98.92 = 0.60
98.69 0.14
VGG16
Color
Purple
97.40 1.49
1 0.62
1
VGGI6
99.82
+ 0.21
+
VGG16
Gramularity
= 0.07
VGG16
Gramularity
99.80
1 0.06
ResNe150
Cell Size
Big
81.23 2.02
87.51
85.79 0.56
ReaNe150
Small
3.89
ResNe150
Cell Shape
Trregular
85.70 2:35
85.48 2.40
85.54 0.55
ResNet50
Cell Shape
Round
93.77
95.80 0.91
96.78 0.22
ResNe150
Nucleus Shupe
Irregular
62.24 0.70
62.37 1 5.10
62.21 1 2.51
ResNe150
Nucleus Shupe
Segmented Bilobed
74.07 4.38
50.30 1 4.59
76.86 1 0.65
ResNe150
Nucleus Shape
Sggmented-Multillee
71.43 4: 3.96
59.97 = 6.11
64.95 + 3.25
ResNet50
Nucleus Shape
Unsegmented- Band
85.42 2.87
85.05 = 4.68
85. + 1.13
ResNet50
Nucleus Shape
Unsogmented-Indeen Indented
35.00
80.50 = 3.33
82.58 + 0.21
ResNet50
Nucleus Shape
Unvegmented Round
84.35 1:
86.01 1 3.79
85.07 1 0.39
ResNet50
NC Ratio
High
96.16
91.11 = 0.14
93.56 + 0.11
ResNe150
NC Ratio
Low
0.00
99.50 = 0.05
99.14 1 0.02
ResNe150
Chromatin Density
Densely
97.97 1:
96.67 1 0.20
97.31 1 0.07
ReaNet50
Chromatin Deasity
Looscly
71.11 1.02
80.37 = 1.30
75.16 + 0.58
ReaNer50
Cytoplasm Vacuole
No
97.89 + 0.37
99.05 = 0.39
98.17 /7 + 0.01
ResNer50
Cytoplasm Vacuole
Yes
87.53 + 3.90
75. + 4.57
80.67 1 0.94
ResNer50
Cytoplasm Texture
Clear
98.78 0.30
96.49 = 0.64
97.62 + 0.25
ResNet50
Cyloplasm Texture
Frosted
87.66 1: 1.92
95.42 1 1.16
91.36 1 0.77
ResNet50
Cyloplasm Color
Blue
83.30 3.68
87.53 1: 4.93
85.19 1 0.49
ResNe150
Cytoplasm Color
Light Blue
99.37 + 0.32
99.09 + U.25
99.23 + 0.09
ResNet50
Cytoplasm Color
Purple Blue
82.07 3.01
77.55 - 6.01
79.54 1.76
ResNet50
Granule Type
Coarse
98.67 0.54
99.52 - 0.31
99.09 + 0.20
ResNet50
Granule Type
99.22 1: 0.28
1
1
ResNe150
Granule Type
99.82
+ 0.
99.86 +
ResNe150
Granule Type
99.74
0.19
ResNe150
Gramule Color
99.34 0.07
1
99.53 1
ReaNe150
Granule Color
Pink
98.66 0.28
0.25
98.77 0.07
ReaNer50
Granule Color
Purple
97.82 +
0.25
ResNe150
Granule Color
Red
99.75 + 0.25
99.93 0.
99.84 0.09
ResNet50
Granularity
No
99.38 + 0.00
99.46 0.07
99.42 0.03
Comtined on next page
27

---

Table 7: Precision, Recall, and F-measure of 16 ResNer50, VIT ViT-Base, and ConyNeXe Tiny (CNXT. Each Attribure Valuo
Backbone
Attribate Name
Attribute Value
Precision
Rocall
F-measure
ResNen50
Granularity
1: 0.02
+ 0.00
1
ViT-Base
Big
83.20 +: 1.36
+ 1.49
85.38 +
VTT-Base
Small
ViT-Base
Cell Shape
Irregular
83.44 1.25
85.10 1.06
84.25 0.78
VTT-Base
Cell Shape
Round
95.63 0.29
0.45
95.35 0.25
VT--Rase
Nucleus Shupe
Trregular
68.62 4: 5.78
64.77 = 18.26
64.79 1 8.39
VT--Base
Nucleus Shape
Segmented-Bilene Billoed
73.59 +: 5.70
76.88 6.31
74.83 1.14
Base
Nucleus Shupe
Segmented Multilobed
69.59 1: 2.87
59.41 1 5.31
63.89 1 1.95
VT Base
Nucleus Shupe
Unvegmented Band
82.96 1: 5.54
84.48 1 7.77
83.31 1 1.72
VT-Base
Nacleus Shape
Unsegmentedd-ndeen
85.63 +: 1.91
80.96 = 2.99
83.17 + 0.65
ViT-Base
Nucleus Shape
Unsegmented Round
82.85 +: 2.36
88.74 + 2.35
85.66 + 1.14
VIT-Base
NC Ratio
High
94.77 + 1.09
92.03 = 1.72
93.61 + 0.52
VIT-Base
NC Ratio
Low
98.98 + 0.23
99.29 = 0.25
99.13 + 0.07
ViT-Base
Chromatin Density
Densely
97.16 +: 0.63
97.10 + 1.04
97.12 1 0.39
ViT-Rase
Chromatin Density
Loosely
72.29 + 5.74
72. = 6.53
71.90 + 3.25
VIT-Base
Cytoplasm Vacuole
No
98.53 +: 0.14
98.51 + 0.25
98.52 1 0.06
VIT-Base
Cytoplasm Vacuole
Yes
82.70 + 2.07
82.80 = 1.64
82.75 + 0.33
ViT-Base
Cytoplasm Texture
Clear
98.39 0.58
96. = 0.35
97.11 + 0.12
ViT-Base
Cytoplasm Texture
Frosted
87.33 0.84
93.91 + 2.26
90.48 1 0.64
ViT-Base
Cytoplasm Color
Blue
86.32 1.59
80.50 = 4.13
83.23 = 1.48
VaseBase
Cyloplasm Color
Light Blue
99.32 1: 0.40
99.21 1 0.13
99.27 1: 0.14
VT VT
Cyloplasm Color
Purple Blue
77.22 1: 1.70 1.70
84.41 1 2.59
80.61 1 0.34
VIT-Base
Granule Type
Coarse
98.57 = 0.52
99.33 = 0.31
98.95 + 0.30
ViT-Base
Granule Type
Nil
99.22 + 0.28
99.38 + U.11
99.30 0.09
VIT-Base
Granule Type
Round
99.89 + 0.00
99.82 - 0.15
99.86 0.08
VIT-Base
Granule Type
Small
99.61 = 0.09
99.28 = 0.05
99.44 0.07
VaseBase
Color
99.46 0.07
1 0.07
99.51 1 0.00
Pink
98.40 + 0.40
99.49 + 0.
0.14
ViT-Base
Granule Color
Purple
+ 0.79
VIT-Base
Granule Color
Red
99.96 0.06
0.06
99.95 0.05
VIT-Base
Granularity
No
99.46 0.06
= 99.81 0.02
VT--Rase
Granolarity
Yes
99.83 0.04
99.82 0.01
CNXT-T
Cell Size
84.92 : 0.64
85.43 = 1.29
85.17 + 0.45
CNXTT
Cell Size
Small
82.09 1: 1.14
81.46 + 1.16
81.76 1 0.37
CNXTT
Cell Shape
Irregular
85.71 1.11
1.11
87.33 1 0.46
86.51 1 0.75
CNXT-T
Cell Shape
Round
96.28 0.14
95.75 = 0.37
96.01 + 0.25
CXXT-T
Nucleus Shape
Irregular
63.15 2.44
63.38 + 7.48
63.15 + 4.57
CNXT-T
Nucleus Shape
Segmented-Be Billoed
75.91 2.21
81.92 = 1.84
78.78 + 1.56
CNXT-T
Nucleus Shape
Sggmeute-Muutillbee
72.04
64.51 = 1.39
08.01 + 2.76
CNXT:T
Nucleus Shape
Unvegmented Band
87.13 2.24
85.48 + 1.02
86.27 1: 0.64
CNXT-T
Nucleus Shape
Unvegmented
Envegmented-Indeenteed Indented
87.44 +: 1.94
81.57 + 1.36
84.38 + 0.47
CNXFT
Nucleus Shupe
UnsegmeeneddRoowe Round
85.38 1.29
89.18 = 0.80
87.23 1 0.50
CNXT-T
NC Ratio
High
91.70
92.00 = 0.49
93.33 + 0.61
CNXT-T
NC Ratio
Low
98.90 0.07
99.29 = 0.11
99.10 + 0.08
CNXT-T
Chromatin Density
Densely
97.39 0.19
97.29 + 0.28
97.34 1 0.07
CNXT-T
Chromatin Density
Loosely
73.73 + 1.50
74.45 = 1.94
74.06 1 0.58
CNXTT
Cyloplasm Vacuole
No
98.05 1: 0.04
99.07 1 0.10
98.55 1: 0.06
CNXTT
Cyloplasm Vacuole
87.62 1: 1.19
77.01 1 0.44
81.97 1 0.73
CNXT-T
Cytoplasm Texture
Clear
98.10 0.50
97.23 = 0.49
97. + 0.10
CNXI-T
Cytoplasm Texture
Frested
89.76 1.49
92.76 + 1.97
91.21 + 0.44
CNXT-T
Cytoplasm
Blue
85.26
+ 1.58
84.57 + 0.69
CNXT-T
Cytoplasm Color
Light
99.28 + 0.36
99.18 = 0.07
99.23 + 0.15
Cytoplasm
Purple
79.60 0.80
1 2.32
1 99.24
CXXI-T
Granule Type
Coarse
CXXITT
Granule Type
99.59 1: 0.07
u.07
CNXT-T
Granule Type
Round
99.89 0.00
99.96 0.06
99.93 0.03
CNXT-T
Granule Type
Small
99.70
99.31
99.52
CNXT:T
Granule Color
Nil
99.63 1 0.00
99.59 + 0.13
99.61 1 0.07
CNXT-T
Granule Color
Pink
98.43 0.05
99.60 = 0.
99.01 1 0.10
CNXTT
Gramule Color
Purple
98.81 1: 0.63
95.81 1 0.14
97.29 1 0.37
CNXTT
Gramule Color
Red
100.00 1: 0.00
100.00 0.00
100.00 1 0.00
CNXT-T
Granularity
No
99.55 + 0.13
99.59 = 0.07
99.57 + 0.06
CNXTT
Granularity
Yes
99.85 + 0.02
99.81 = 0.05
99.85 + 0.02
28

---

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29


Reviews:
[{"review_id": "QsrUEeuuE2", "reviewer": "Reviewer_U8Fk", "summary": "", "questions": "", "limitations": "Yes.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "The dataset is large and annotated in detail. xAI for histology is a very relevant topic for clinical applications.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "6DzecjuLjg", "reviewer": "Reviewer_sgnA", "summary": "", "questions": "", "limitations": "Please refer to above points.", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The paper introduces a large-scale dataset of White Blood Cells (WBCs) that is densely annotated with 11 morphological attributes. These attributes provide valuable information about the cell and its components (nucleus, cytoplasm, and granules), essential for diagnosing blood-related diseases such as leukaemia and anaemia.\n2. The paper provides detailed descriptions of the data collection, annotation, and analysis processes, ensuring the dataset's quality and reliability.\n3. The paper demonstrates the usefulness of these attributes for various tasks, such as WBC classification, attribute prediction, and attribute-based retrieval. These tasks can help clinicians to make more accurate and informed decisions when diagnosing blood-related diseases.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "I5JBwdSCSQ", "reviewer": "Reviewer_mmqD", "summary": "", "questions": "", "limitations": "It would be great if authors could show some experimental results of the method which copes with the dataset annotations noises. ", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "+ New detailed attributes annotations of white blood cells.\n+ Detailed reasoning for each attribute is provided\n+ The authors discussed interesting applications of annotations.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "L6jUvYsNYN", "reviewer": "Reviewer_ZbQt", "summary": "", "questions": "", "limitations": "The authors have already mentioned a few limitations such as only containing healthy cells and data from a single source with a single staining method. It can affect how widely this dataset will be used.", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- High quality and clinically relevant labels of white blood cell morphology\n- Reasonable distribution of different attributes without any being highly skewed\n- The authors explored interesting uses of this dataset (XAI, StyleGAN, etc.)", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: This paper introduces WBCAtt, a novel dataset for white blood cell (WBC) recognition that is densely annotated with 11 morphological attributes. These attributes are essential for explaining how hematologists recognize WBCs and diagnose blood-related diseases. The group leveraged an existing dataset and added rich annotations of the WBCs to enable explanation of a classification.
The dataset highly relies on the quality of the annotation, which was well-described and appears to be robust. The development of downstream models are improved by providing explainability. Concerns from the reviewers were adequately by the authors.
Decision: Accept (Poster)