Selective Prediction Forgetting for Adaptation of Weakly Supervised Localization of Histology Images
Authors: Alexis Guichemerre, Banafsheh Karimian, Soufiane Belharbi, Natacha Gillet, Nicolas Thome, Pourya Shamsolmoali, Mohammadhadi Shateri, Luke McCaffrey, Eric Granger
Organizations: ÉTS Montréal, Montreal, Canada · Sorbonne Université, Paris, France · University of York, York, United Kingdom · McGill University, Montreal, Canada
Under strong cross-domain shifts, weakly supervised object localization (WSOL) models often lose their capacity to discriminate and develop a strong prediction bias toward a subset of classes. As a result, the majority of target samples are assigned to these dominant classes. Since source-free (unsupervised) domain adaptation (SFDA) methods typically rely on pseudo-labels, inaccurate pseudo-label estimation causes suboptimal adaptation and poor classification and localization performance. This issue is particularly challenging in computational histopathology, where shifts in organ-specific morphology, staining protocols, and acquisition conditions can significantly impact class-discriminative visual cues. This paper introduces SFDA with selective prediction forgetting (SFDA-SPF), a method designed to mitigate excessive concentration of predictions for dominant classes during target-domain adaptation. SFDA-SPF identifies prediction-dominant classes based on the model's target predictions and selectively revises uncertain samples assigned to them. Importantly, this procedure does not require knowledge of the true target class distribution. In contrast with previous SFDA methods, SFDA-SPF introduces a 'forget set' composed of uncertain samples from dominant classes near their decision boundaries, enabling the classification boundaries to shift away from biased predictions. A second 'retain set' is used to anchor certain and accurate samples. To forget the predictions of dominant classes, SFDA-SPF introduces a complementary optimization objective that reduces their probability while allowing alternative class assignments to emerge. This iterative optimization process reassesses target images for forget and retain sets at regular intervals. In addition to improving image-level pseudo-label estimation, the pixel-level classifier is jointly optimized, further enhancing localization.
Figures & tables
Figure 1: (a) SFDA-SPF reduces predictive bias across centers. (b) Source-only inference shows a strong predictive bias toward the cancer class when transferring from GlaS (source) to CAMELYON16 and CAMELYON17 (targets) with PixelCAM. (c) In contrast, traditional SFDA methods, such as SFDA-DE , amplify the bias and often collapse toward a dominant class.
Figure 2: Conceptual overview of SFDA-SPF . In source-free adaptation for histological WSOL, the source model is biased toward the dominant class. (a) Source model: biased prediction. Many samples lie near the decision boundary, where high-entropy predictions are informative but error-prone. (b) Standard SFDA: trust pseudo-labels. Conventional methods reinforce confident pseudo-labels. However, in histology, confidence does not necessarily imply correctness, and erroneous pseudo-labels amplify the initial bias and attract uncertain samples toward the dominant class. (c) SFDA-SPF forget uncertain samples (ours). We instead identify uncertain dominant-class samples as a forget set . The forgetting objective reduces the probability assigned to the current dominant-class prediction, allowing probability mass to be redistributed toward alternative classes.
Figure 3: Overview of the proposed SFDA-SPF pipeline. Starting from a source-pretrained WSOL model and an unlabeled target set, SFDA-SPF first identifies dominant classes from the target prediction distribution. For each dominant class, the fraction ρ of samples with the highest normalized entropy is assigned to the forget set, while the remaining target samples form the retain set. During adaptation, LF reduces the probability assigned to the current dominant-class predictions of forget samples, whereas LR preserves the current assignments of the remaining samples to limit excessive model drift. An auxiliary pixel-level loss, LL , uses CAM-based partial pseudo-labels from the frozen source model to preserve spatial representations. Target predictions and the dominant, forget, and retain sets are periodically recomputed as the decision boundary evolves.
C16
C17-0
C17-1
C17-2
C17-3
C17-4
Avg
Method
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PixelCAM (MIDL’25)
Source only
37.9
51.8
37.2
41.2
27.2
58.7
44.9
64.9
43.1
39.1
31.1
39.9
36.9
49.3
SHOT (ICML’20)
14.9
67.1
12.1
71.7
19.2
70.9
24.4
71.6
15.1
78.9
13.9
56.4
16.6
69.4
SFDA-DE (CVPR’22)
37.9
51.8
14.5
50.0
36.7
65.7
28.9
70.5
18.9
49.9
31.1
39.9
28.0
54.6
CDCL (TMM’22)
37.9
51.8
25.3
50.0
21.2
59.3
25.5
69.9
19.3
49.9
22.6
40.9
25.3
53.6
ERL (ICLR’23)
29.9
59.7
13.2
59.5
14.6
67.4
43.0
69.4
19.3
49.9
32.4
54.0
25.4
60.0
Table 1: Localization ( PxAP ) and classification ( CL ) accuracy. Results shown for GlaS → C16 , C17-0 , C17-1 , C17-2 , C17-3 , C17-4 .
C17-1
C17-2
C17-3
C17-4
Methods
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PixelCAM (MIDL’25)
SHOT (ICML’20)
22.9
52.9
31.6
77.0
46.8
88.5
51.4
74.1
SFDA-DE (CVPR’22)
23.9
49.4
18.7
50.0
50.7
92.0
51.4
74.1
CDCL (TMM’22)
40.1
73.8
30.6
74.7
59.4
88.3
50.2
83.2
ERL (ICLR’23)
17.4
62.2
45.6
58.4
50.7
92.0
51.4
74.1
RGV (CVPR’25)
36.1
56.9
41.2
50.00
50.7
92.0
54.3
70.4
Table 2: Localization ( PxAP ) and classification ( CL ) accuracy. Inter-center SFDA on CAMELYON17 . The source model is trained on center 0 and independently adapted to centers 1–4.
50:50
60:40
70:30
80:20
90:10
Methods
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PxAP
CL
PixelCAM (MIDL’25)
SHOT (ICML’20)
19.2
70.0
22.9
41.6
37.2
41.2
24.2
41.6
30.5
73.6
CDCL (TMM’22)
25.3
50.0
25.5
45.4
22.6
44.6
22.4
44.6
22.8
44.6
ERL (ICLR’23)
13.2
59.5
21.8
44.2
20.4
43.1
21.5
43.5
21.0
43.5
RGV (CVPR’25)
31.0
50.0
33.7
50.0
33.3
50.0
33.1
50.0
32.8
50.0
Ours
49.9
86.2
43.7
79.0
55.1
82.8
46.0
79.4
58.8
82.8
Table 3: Robustness to class imbalance in the target adaptation set on CAMELYON17 . Starting from the balanced setting ( 50:50 ), we progressively increase the proportion of Normal samples up to 90:10 . The test set remains balanced across all settings; only the target training set is imbalanced.
Figure 4: Effect of dynamic resampling frequency during adaptation with PixelCAM .
LR
LF
LL
PxAP ↑
CL ↑
✓
27.6
41.2
✓
10.4
85.1
✓
9.5
48.1
✓
✓
13.1
84.7
✓
✓
✓
49.9
86.1
Table 4: Impact of loss components during SFDA-SPF on GlaS → C17-0 with PixelCAM.
Figure 5: Ablation analysis of SFDA-SPF on GlaS → C17-3 with PixelCAM . (a) Impact of the localization loss during adaptation. (b) Comparison between static sets , where forget and retain sets are constructed once before adaptation, and dynamic resampling are periodically reconstructed.
Figure 6: Evolution of retain- and forget-set selection accuracy during GlaS → C17-4 adaptation with DeepMIL . The forget and retain sets are dynamically reconstructed every five epochs.
Appendix figures & tables35 assets
Supplementary material from the paper’s appendix.
Appendix
GlaS
C16
C17-0
C17-1
C17-2
C17-3
C17-4
Train
67
24348
634
1066
498
816
940
Val (CL)
18
8850
144
38
110
102
146
Val ( PxAP )
6
10
10
10
10
10
10
Test
80
15664
262
172
448
376
298
Appendix
Table 5: Dataset splits used in our experiments, showing the number of patches per split for each dataset and center.
Figure 8: Representative cancer examples from the training split of the constructed CAMELYON17 WSOL benchmark across the five centers. Ground-truth tumor regions are overlaid in red.
Figure 9: Representative cancer examples from the testing split of the constructed CAMELYON17 WSOL benchmark across the five centers. Ground-truth tumor regions are overlaid in red.
Class
Dataset
Source/Target
Train
Val
Test
Normal
152
76
46
15
15
Polyp
948
474
284
95
95
Low-grade IN
1,276
638
382
128
128
High-grade IN
372
186
112
37
37
Serrated adenoma
116
58
34
12
12
Adenocarcinoma
1,590
795
477
159
159
Appendix
Table 6: Class distribution of the EBHI dataset and the constructed source/target splits.
Category
Corruption
Parameters
Staining
Stain-Light
θ=0.05
Stain-Heavy
θ=0.10
Contamination
Dust
intensity =0.7 , Nsmudges∈[3,10]
Air Bubble
Nbubbles∈[10,20] , α=0.3 , blur severity =2
Blurring
Defocus Blur
severity =5 , radius =10 , alias blur =0.5
Motion Blur
severity =5 , radius =20 , σ=15
Appendix
Table 7: Parameters used to construct the corrupted EBHI-Seg target domains. We follow the official Histopath-C implementation [ Noori et al., 2026 ] .
Figure 10: Examples from the training split of the constructed EBHI-S WSOL benchmark.
Figure 11: Examples from the testing split of the constructed EBHI-S WSOL benchmark.
Figure 12: Examples from the testing split of the constructed CAMELYON17 WSOL benchmark.
Hyperparameter
Value
Backbones
ResNet50 / DeiT
Optimizer
SGD
Nesterov acceleration
True
Momentum
∈{0.1,0.4,0.9} for source models
Weight decay
0.0001
Learning rate
∈{0.0001,0.001,0.002,0.01,0.02,0.1} for source
Appendix
Table 8: General hyperparameters used during source model training.
Hyperparameter
Value
Momentum
0.9
Weight decay
0.0001
Learning rate
∈{0.000001,0.00001,0.0001,0.001}
Learning rate decay
CAMELYON17 : 0.1 ∈{20} epochs
CAMELYON16 : 0.1 ∈5 epochs
Flip ratio (forgotten samples)
∈{0.05,0.15}
Appendix
Table 9: General hyperparameters used during SFDA training.
Hyperparameter
Value
SHOT
λent∈{0.5,1.0}
λdiv∈{0.5,1.0}
λpeu∈{0.5,1.0}
CDCL
τ∈{0.04,0.08,0.5}
λcdcl∈{0.5,1.0}
SFDE
τ∈{0.8,1.0}
Appendix
Table 10: Per-method hyperparameters for SFDA methods.
Method
C17-0 → GlaS
C17-1 → GlaS
C17-2 → GlaS
C17-3 → GlaS
C17-4 → GlaS
Average
SHOT
52.50 / 62.14
55.00 / 59.33
48.75 / 56.84
53.75 / 66.37
53.75 / 56.83
52.75 / 60.30
SFDA-DE
53.75 / 63.54
50.00 / 56.22
55.00 / 55.90
53.75 / 66.37
38.75 / 59.71
50.25 / 60.35
CDCL
53.75 / 63.64
46.25 / 52.55
47.50 / 55.60
53.75 / 64.25
46.25 / 52.70
49.50 / 57.75
ERL
53.75 / 56.92
70.00 / 59.40
56.25 / 56.21
53.75 / 43.52
51.25 / 60.24
57.00 / 55.26
RGV
53.75 / 64.84
53.75 / 50.40
55.00 / 55.90
53.75 / 63.72
53.75 / 60.62
54.00 / 59.10
SFDA-SPF
53.75 / 53.11
77.50 / 56.62
61.25 / 59.83
53.75 / 62.04
55.00 / 59.87
60.25 / 58.29
Appendix
Table 11: Reverse cross-dataset adaptation from each CAMELYON17 center to GlaS . Each entry reports classification accuracy/localization PxAP . The last column reports the average across the five CAMELYON17 source centers.
Method
50:50
60:40
70:30
80:20
90:10
Average
CL / PxAP
CL / PxAP
CL / PxAP
CL / PxAP
CL / PxAP
CL / PxAP
CDCL
50.0 / 25.3
44.6 / 23.4
48.1 / 25.3
46.6 / 25.2
46.6 / 25.5
47.2 / 24.9
SHOT
70.0 / 19.2
41.2 / 37.2
41.2 / 37.2
41.2 / 37.2
41.2 / 37.2
47.0 / 33.6
ERL
59.5 / 13.2
43.1 / 28.2
44.3 / 21.9
43.9 / 22.1
43.9 / 21.7
46.9 / 21.4
RGV
50.0 / 31.0
50.0 / 34.2
50.0 / 34.3
50.0 / 34.9
50.0 / 35.3
50.0 / 33.9
SFDA-SPF
86.2 / 49.9
82.1 / 47.6
83.2 / 50.9
83.9 / 49.7
84.7 / 50.2
84.0 / 49.7
Appendix
Table 12: Robustness to cancer-majority class imbalance in the target adaptation (train) set on CAMELYON17 . Starting from the balanced setting ( 50:50 ), we progressively increase the Cancer/Normal ratio up to 90:10 . Results are reported as classification accuracy ( CL ) / localization PxAP (%). Best results are shown in bold .
Target
p=0.01
p=0.05
p=0.10
CL / PxAP
CL / PxAP
CL / PxAP
C17-0 → 1
67.44 / 25.37
74.42 / 34.69
75.58 / 40.59
C17-0 → 2
78.35 / 48.17
79.24 / 54.34
79.02 / 59.12
C17-0 → 3
92.29 / 63.45
93.88 / 60.44
93.09 / 62.39
C17-0 → 4
83.22 / 46.13
79.87 / 48.88
77.85 / 47.43
Appendix
Table 13: Sensitivity to the forgetting proportion p under inter-center adaptation on CAMELYON17 . The source model is trained on center 0 and independently adapted to centers 1–4. Results are reported as classification accuracy (CL) / localization PxAP (%). Best results for each target are shown in bold .
Target
Ratio of Images for Localization
0.2
0.5
1.0
CL / PxAP
CL / PxAP
CL / PxAP
C17-1
68.02 / 37.95
75.58 / 40.59
74.42 / 34.69
C17-2
79.24 / 59.38
79.02 / 53.47
79.24 / 54.34
C17-3
92.29 / 63.45
93.35 / 60.61
93.88 / 60.44
C17-4
83.22 / 46.13
78.52 / 38.03
79.87 / 48.88
Appendix
Table 14: Sensitivity to the localization keep ratio on CAMELYON17 inter-center adaptation using center 0 as source. Results are reported as classification accuracy ( CL ) / localization PxAP (%).
Backbone
Classifier
PxAP ↑
CL ↑
Frozen
Trainable
31.4
47.7
Trainable
Frozen
31.4
85.8
Trainable
Trainable
49.9
86.1
Appendix
Table 15: Impact of jointly adapting the backbone and image-level classifier. Results are reported on GlaS → C17-0 with PixelCAM . The source model used for CAM-based pixel pseudo-label generation remains frozen in all configurations.
Figure 13: Entropy distributions of correctly and incorrectly classified samples predicted as cancer by SAT.
SHOT (ICML’20)
CDCL (TMM’22)
ERL (ICLR’23)
RGV (CVPR’25)
Ours
Target condition
PxAP
F1
PxAP
F1
PxAP
F1
PxAP
F1
PxAP
F1
Target clean
83.7
77.2
83.7
77.2
83.7
77.2
83.7
77.2
89.3
82.3
Stain-light
83.5
75.1
83.8
67.1
83.7
69.9
83.8
67.1
90.9
75.1
Stain-heavy
86.7
70.3
81.3
55.9
70.5
56.7
80.8
43.9
82.7
57.9
Dust
82.9
79.4
83.1
71.0
83.1
71.0
83.1
71.0
86.7
72.4
Air bubble
77.8
78.1
76.8
45.3
76.8
45.3
76.8
45.3
78.4
66.5
Appendix
Table 16: Localization ( PxAP ) and classification (F1) performance on EBHI-T under controlled domain shifts. The source model is trained on the clean source domain and independently adapted to each target condition. For all methods, results are reported at the checkpoint selected according to the best F1 score. Best results are shown in bold and second-best results are underlined .
Target Clean (Shift 1)
Brightness (Shift 10)
Defocus Blur (Shift 6)
Contrast (Shift 11)
Class
GT
GT (%)
Rec.
Prec.
F1
Pred.
Rec.
Prec.
F1
Pred.
Rec.
Prec.
F1
Pred.
Rec.
Prec.
F1
Pred.
Normal
22
3.3
13.6
100.0
24.0
3 (0.5%)
18.2
80.0
29.6
5 (0.8%)
0.0
0.0
0.0
0 (0.0%)
0.0
0.0
0.0
0 (0.0%)
Polyp
143
21.4
94.4
83.9
88.8
161 (24.1%)
95.1
81.0
87.5
168 (25.2%)
10.5
65.2
18.1
23 (3.4%)
6.3
81.8
11.7
11 (1.6%)
Low-grade IN
191
28.6
91.1
91.1
91.1
191 (28.6%)
81.2
92.8
86.6
167 (25.0%)
0.0
0.0
0.0
0 (0.0%)
0.0
0.0
0.0
0 (0.0%)
High-grade IN
55
8.2
69.1
73.1
71.0
52 (7.8%)
69.1
64.4
66.7
59 (8.8%)
90.9
9.4
17.0
535 ( 80.2% )
98.2
8.8
16.1
614 ( 92.1% )
Serrated adenoma
17
2.5
94.1
94.1
94.1
17 (2.5%)
94.1
94.1
94.1
17 (2.5%)
5.9
100.0
11.1
1 (0.1%)
0.0
0.0
0.0
0 (0.0%)
Appendix
Table 17: Per-class source-model predictions under representative target shifts on the EBHI training set. For each class, we report the ground-truth distribution (GT), per-class recall (Rec.), precision (Prec.), F1-score, and predicted class distribution (Pred.). Target Clean and Brightness represent conditions where the source model preserves predictions across classes, whereas Defocus Blur and Contrast illustrate severe prediction collapse.
Figure 14: Reliability of high-confidence foreground regions for GlaS → CAMELYON17 . For each positive target image, CAM pixels are ranked according to their activation score, and foreground precision is computed as the fraction of top-ranked pixels overlapping the ground-truth foreground mask. Results are reported for PixelCAM, SAT, and DeepMIL across the five CAMELYON17 centers.
Figure 15: Reliability of high-confidence foreground regions under CAMELYON17 inter-center domain shifts using center 0 as the source. The source models are evaluated on centers 1–4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 16: Reliability of high-confidence foreground regions under CAMELYON17 inter-center domain shifts using center 1 as the source. The source models are evaluated on centers 0, 2, 3, and 4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 17: Reliability of high-confidence foreground regions under CAMELYON17 inter-center domain shifts using center 2 as the source. The source models are evaluated on centers 0, 1, 3, and 4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 18: Reliability of high-confidence foreground regions under CAMELYON17 inter-center domain shifts using center 3 as the source. The source models are evaluated on centers 0, 1, 2, and 4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 19: Reliability of high-confidence foreground regions under CAMELYON17 inter-center domain shifts using center 4 as the source. The source models are evaluated on centers 0–3. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 20: Reliability of low-activation background regions for GlaS → CAMELYON17 . For each positive target image, CAM pixels are ranked according to their activation score, and background precision is computed as the fraction of the lowest-activation pixels overlapping the ground-truth background region. Results are reported for PixelCAM, SAT, and DeepMIL across the five CAMELYON17 centers.
Figure 21: Reliability of low-activation background regions under CAMELYON17 inter-center domain shifts using center 0 as the source. The source models are evaluated on centers 1–4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 22: Reliability of low-activation background regions under CAMELYON17 inter-center domain shifts using center 1 as the source. The source models are evaluated on centers 0, 2, 3, and 4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 23: Reliability of low-activation background regions under CAMELYON17 inter-center domain shifts using center 2 as the source. The source models are evaluated on centers 0, 1, 3, and 4. Results are reported for PixelCAM, SAT, and DeepMIL.
Figure 24: Distribution of correct and incorrect predictions across predictive-entropy bins under different domain shifts and target class priors. Results are shown for PixelCAM . Bars report the percentage of target samples within each normalized entropy bin, decomposed into correctly (blue) and incorrectly (orange) classified samples.
Figure 25: Relationship between prediction entropy and classification accuracy under CAMELYON17 inter-center domain shifts using center 2 as the source. Results are reported for PixelCAM .
Figure 26: Relationship between prediction entropy and classification accuracy under CAMELYON17 inter-center domain shifts using center 2 as the source. Results are reported for SAT .
Figure 27: Localization produced by the PixelCAM WSOL model after SFDA-SPF and the state-of-the-art SFDA methods ( GlaS → CAMELYON17-3 ).
Figure 28: Localization produced by the SAT WSOL model after SFDA-SPF and the state-of-the-art SFDA methods ( GlaS → CAMELYON17-3 ).
Figure 29: Localization produced by the DeepMIL WSOL model after SFDA-SPF and the state-of-the-art SFDA methods ( GlaS → CAMELYON17-3 ).
Pathology foundation models (PFMs) support a wide range of histopathology tasks on whole-slide images through multiple instance learning (MIL). Despite recent progress, these models remain sensitive to cross-hospital distribution shifts: performance often degrades when a task head (e.g., classification) is trained on data from one hospital and then used on data from another hospital. To address this challenge, we propose AdaptMIL, a training strategy for integrating domain adaptation into MIL training. AdaptMIL is motivated by our finding that directly applying domain adaptation objectives during MIL training can interfere with MIL aggregation learning, often resulting in performance degradation over standard training. AdaptMIL resolves this by decoupling representation-level domain alignment learning from MIL aggregation learning through a two-stage training procedure. We show that AdaptMIL improves cross-hospital performance and outperforms alternative adaptation methods across experiments spanning multiple WSI datasets, tasks, PFMs, MIL architectures, and domain adaptation objectives. AdaptMIL's code will be released upon publication.
Ben Vardi, Dana Schonberger, Yuval Friedmann +4
Reichman University · Institute of Pathology, Sheba Medical Center · Technion – Israel Institute of Technology +1
Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifiers for a range of histopathology tasks. However, their robustness across hospitals remains limited: performance often degrades when training a classifier on data from one hospital and evaluating it on another target hospital. We address this challenge by fine-tuning PFMs with a local maximum mean discrepancy (LMMD) objective that applies to two settings: domain adaptation, where unlabeled target-hospital data is available, and domain generalization, where target-hospital data is unavailable at all. Experiments at both the patch- and slide-level show consistent improvements across multiple PFMs and tasks.
Ben Vardi, Dana Schonberger, Yuval Friedmann +4
Reichman University, Herzliya, Israel · Institute of Pathology, Sheba Medical Center, Ramat-Gan, Israel · Technion - Israel Institute of Technology, Haifa, Israel
Existing weakly supervised semantic segmentation (WSSS) methods in computational pathology rely on a multi-stage paradigm: class activation map (CAM) generation, offline pseudo-mask refinement, and fully supervised retraining. While established, this decoupled approach presents fundamental limitations. The multi-stage process not only incurs high computational training costs but also suffers from error propagation: local texture biases in shallow CNN layers generate false-positive artifacts that subsequent refinement steps often fail to correct. To address these persistent challenges through a simple yet highly effective approach, we propose the Single-Stage Hierarchical Rectification (SSHR) framework. Rather than passively refining CAMs post-hoc, our method proactively purifies intermediate feature representations during the forward pass. We introduce a Hierarchical Feature Rectification Module (HFRM) that utilizes deep global semantic context to filter out local anomalies in shallow layers. This mechanism generates high-fidelity activation maps directly within a single training loop. Experiments on the LUAD-HistoSeg and BCSS datasets demonstrate that SSHR outperforms state-of-the-art multi-stage methods. Furthermore, SSHR reduces training duration by 2 to 5 times. This efficiency minimizes computational overhead and accelerates clinical translation for large-scale histopathology workflows. The code is available at: https://github.com/trongduc-nguyen/SSHR
Duc T. Nguyen, Hoang-Long Nguyen, Thanh-Ha DO +1
College of Engineering & Computer Science, VinUniversity, Hanoi, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam · The Computer Vision and Medical AI Lab, VinUniversity, Hanoi, Vietnam +1