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From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology

11 Apr 2022arXiv:2204.05044archive 2025-07-28

Maximilian Springenberg, Annika Frommholz, Markus Wenzel, Eva Weicken, Jackie Ma, Nils Strodthoff

While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy. In order to fill this gap, we developed a new methodology to extensively evaluate a wide range of classification models, including recent vision transformers, and convolutional neural networks such as: ConvNeXt, ResNet (BiT), Inception, ViT and Swin transformer, with and without supervised or self-supervised pretraining. We thoroughly tested the models on five widely used histopathology datasets containing whole slide images of breast, gastric, and colorectal cancer and developed a novel approach using an image-to-image translation model to assess the robustness of a cancer classification model against stain variations. Further, we extended existing interpretability methods to previously unstudied models and systematically reveal insights of the models' classifications strategies that can be transferred to future model architectures.

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3ran · our draft was wrong
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collect_scores hhi-aml/histobenchmark/code/main_cls_scores.py official repository ran · our draft was wrong no licence file found · pointer only · 0d4eb2eb9e32af69 · report
get_paramgroups hhi-aml/histobenchmark/code/main_patho_lightning.py official repository ran · our draft was wrong no licence file found · pointer only · e8f19915080b148b · report
mixup_criterion hhi-aml/histobenchmark/code/main_patho_lightning.py official repository ran · our draft was wrong no licence file found · pointer only · 97f484048ac03556 · report
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integrated_top_K hhi-aml/histobenchmark/code/main_heatmaps.py official repository unverified no licence file found · pointer only · 5e4c10ee1d00a8c3 · report

Tasks

BenchmarkingCancer ClassificationClassificationImage-to-Image Translationwhole slide images

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Methods

ConvNeXt

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