A systematic review of deep learning models trained and tested using the HAM10000 dataset: an overview of recent advancements and challenges Article Swipe
Gokul Raghavendra Srinivasan
,
Jason R. McFadden
,
Yunrui Lu
,
Matthew Davis
,
Joshua Levy
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7834480
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7834480
Objective: Recent advances in sophisticated computer vision techniques have accelerated the development of methods for interpreting dermatological images. While a plethora of studies have been published in the last few years, the sheer volume of published work has made it challenging to determine which approaches are state-of-the-art, and what sorts of potential limitations warrant further study. Deep learning algorithms developed for the HAM10000 dataset, a large dataset of dermoscopic images, exemplifies the burgeoning development and research of artificial intelligence (AI) applications adapted for dermatological imaging, where papers are published every other week. This systematic review aims to provide a comprehensive survey of deep learning models trained and tested on the HAM10000 dataset, paying special attention to their innovations and limitations, alongside broader qualitative perspectives concerning the direction of progress and challenges for research utilizing this dataset. In so doing, readers will gain a deeper appreciation for current HAM10000 benchmarks, as well as context for future findings. Methods: We conducted an exhaustive query of MEDLINE and PubMed Central (PMC) databases through PubMed using keywords including ‘HAM10000’, ‘HAM-10000’, and “HAM 10000”. Studies published between 2018 (the year of inception for the dataset) and 2022 were included. Results: A total of 31 studies met inclusion criteria for this review, with each study falling in to at least one of six categories: feasibility, classification, segmentation, distributed computation, and validation. Across all categories, model accuracy ranged from 0.84 to 0.99. The most common model used in the literature is CNNs, with DenseNet, in particular, being especially common. The most common reason for exclusion of a study was biased analysis with overlap of training/testing sets. Conclusions: 1) attention matters as well as manually segmenting areas of interest prior to classification, 2) transfer learning often provide additional performance gains versus model architecture changes, and there are several 3) validation and 4) user-centered design challenges which must be addressed prior to widespread implementation. Taken together, balancing clinical intuition with technical quantitative expertise and addressing these challenges can lead to the development of more accurate and clinically useful deep learning models for dermatological images.
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A systematic review of deep learning models trained and tested using the HAM10000 dataset: an overview of recent advancements and challengesWork title
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2023Year of publication
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2023-04-16Full publication date if available
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Gokul Raghavendra Srinivasan, Jason R. McFadden, Yunrui Lu, Matthew Davis, Joshua LevyList of authors in order
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| abstract_inverted_index.models | 104, 341 |
| abstract_inverted_index.papers | 86 |
| abstract_inverted_index.paying | 112 |
| abstract_inverted_index.ranged | 230 |
| abstract_inverted_index.reason | 255 |
| abstract_inverted_index.review | 94 |
| abstract_inverted_index.study. | 55 |
| abstract_inverted_index.survey | 100 |
| abstract_inverted_index.tested | 107 |
| abstract_inverted_index.useful | 338 |
| abstract_inverted_index.versus | 292 |
| abstract_inverted_index.vision | 6 |
| abstract_inverted_index.volume | 33 |
| abstract_inverted_index.years, | 30 |
| abstract_inverted_index.“HAM | 177 |
| abstract_inverted_index.Central | 166 |
| abstract_inverted_index.MEDLINE | 163 |
| abstract_inverted_index.Studies | 179 |
| abstract_inverted_index.adapted | 81 |
| abstract_inverted_index.between | 181 |
| abstract_inverted_index.broader | 121 |
| abstract_inverted_index.common. | 251 |
| abstract_inverted_index.context | 152 |
| abstract_inverted_index.current | 146 |
| abstract_inverted_index.dataset | 66 |
| abstract_inverted_index.falling | 209 |
| abstract_inverted_index.further | 54 |
| abstract_inverted_index.images, | 69 |
| abstract_inverted_index.images. | 17, 344 |
| abstract_inverted_index.matters | 272 |
| abstract_inverted_index.methods | 13 |
| abstract_inverted_index.overlap | 265 |
| abstract_inverted_index.provide | 97, 288 |
| abstract_inverted_index.readers | 139 |
| abstract_inverted_index.review, | 205 |
| abstract_inverted_index.several | 299 |
| abstract_inverted_index.special | 113 |
| abstract_inverted_index.studies | 22, 199 |
| abstract_inverted_index.through | 169 |
| abstract_inverted_index.trained | 105 |
| abstract_inverted_index.warrant | 53 |
| abstract_inverted_index.HAM10000 | 62, 110, 147 |
| abstract_inverted_index.accuracy | 229 |
| abstract_inverted_index.accurate | 335 |
| abstract_inverted_index.advances | 2 |
| abstract_inverted_index.analysis | 263 |
| abstract_inverted_index.changes, | 295 |
| abstract_inverted_index.clinical | 318 |
| abstract_inverted_index.computer | 5 |
| abstract_inverted_index.criteria | 202 |
| abstract_inverted_index.dataset) | 189 |
| abstract_inverted_index.dataset, | 63, 111 |
| abstract_inverted_index.dataset. | 135 |
| abstract_inverted_index.imaging, | 84 |
| abstract_inverted_index.interest | 280 |
| abstract_inverted_index.keywords | 172 |
| abstract_inverted_index.learning | 57, 103, 286, 340 |
| abstract_inverted_index.manually | 276 |
| abstract_inverted_index.plethora | 20 |
| abstract_inverted_index.progress | 128 |
| abstract_inverted_index.research | 75, 132 |
| abstract_inverted_index.transfer | 285 |
| abstract_inverted_index.10000”. | 178 |
| abstract_inverted_index.DenseNet, | 246 |
| abstract_inverted_index.addressed | 310 |
| abstract_inverted_index.alongside | 120 |
| abstract_inverted_index.attention | 114, 271 |
| abstract_inverted_index.balancing | 317 |
| abstract_inverted_index.conducted | 158 |
| abstract_inverted_index.databases | 168 |
| abstract_inverted_index.determine | 42 |
| abstract_inverted_index.developed | 59 |
| abstract_inverted_index.direction | 126 |
| abstract_inverted_index.exclusion | 257 |
| abstract_inverted_index.expertise | 323 |
| abstract_inverted_index.findings. | 155 |
| abstract_inverted_index.inception | 186 |
| abstract_inverted_index.included. | 193 |
| abstract_inverted_index.including | 173 |
| abstract_inverted_index.inclusion | 201 |
| abstract_inverted_index.intuition | 319 |
| abstract_inverted_index.potential | 51 |
| abstract_inverted_index.published | 25, 35, 88, 180 |
| abstract_inverted_index.technical | 321 |
| abstract_inverted_index.together, | 316 |
| abstract_inverted_index.utilizing | 133 |
| abstract_inverted_index.</strong>A | 195 |
| abstract_inverted_index.additional | 289 |
| abstract_inverted_index.addressing | 325 |
| abstract_inverted_index.algorithms | 58 |
| abstract_inverted_index.approaches | 44 |
| abstract_inverted_index.artificial | 77 |
| abstract_inverted_index.burgeoning | 72 |
| abstract_inverted_index.challenges | 130, 306, 327 |
| abstract_inverted_index.clinically | 337 |
| abstract_inverted_index.concerning | 124 |
| abstract_inverted_index.especially | 250 |
| abstract_inverted_index.exhaustive | 160 |
| abstract_inverted_index.literature | 242 |
| abstract_inverted_index.segmenting | 277 |
| abstract_inverted_index.systematic | 93 |
| abstract_inverted_index.techniques | 7 |
| abstract_inverted_index.validation | 301 |
| abstract_inverted_index.widespread | 313 |
| abstract_inverted_index.</strong>1) | 270 |
| abstract_inverted_index.</strong>We | 157 |
| abstract_inverted_index.accelerated | 9 |
| abstract_inverted_index.benchmarks, | 148 |
| abstract_inverted_index.categories, | 227 |
| abstract_inverted_index.categories: | 217 |
| abstract_inverted_index.challenging | 40 |
| abstract_inverted_index.dermoscopic | 68 |
| abstract_inverted_index.development | 11, 73, 332 |
| abstract_inverted_index.distributed | 221 |
| abstract_inverted_index.exemplifies | 70 |
| abstract_inverted_index.innovations | 117 |
| abstract_inverted_index.limitations | 52 |
| abstract_inverted_index.particular, | 248 |
| abstract_inverted_index.performance | 290 |
| abstract_inverted_index.qualitative | 122 |
| abstract_inverted_index.validation. | 224 |
| abstract_inverted_index.applications | 80 |
| abstract_inverted_index.appreciation | 144 |
| abstract_inverted_index.architecture | 294 |
| abstract_inverted_index.computation, | 222 |
| abstract_inverted_index.feasibility, | 218 |
| abstract_inverted_index.intelligence | 78 |
| abstract_inverted_index.interpreting | 15 |
| abstract_inverted_index.limitations, | 119 |
| abstract_inverted_index.perspectives | 123 |
| abstract_inverted_index.quantitative | 322 |
| abstract_inverted_index.comprehensive | 99 |
| abstract_inverted_index.segmentation, | 220 |
| abstract_inverted_index.sophisticated | 4 |
| abstract_inverted_index.user-centered | 304 |
| abstract_inverted_index.dermatological | 16, 83, 343 |
| abstract_inverted_index.classification, | 219, 283 |
| abstract_inverted_index.implementation. | 314 |
| abstract_inverted_index.‘HAM10000’, | 174 |
| abstract_inverted_index.<strong>Methods: | 156 |
| abstract_inverted_index.<strong>Results: | 194 |
| abstract_inverted_index.training/testing | 267 |
| abstract_inverted_index.‘HAM-10000’, | 175 |
| abstract_inverted_index.state-of-the-art, | 46 |
| abstract_inverted_index.<strong>Conclusions: | 269 |
| abstract_inverted_index.<strong>Objective:</strong> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.04180258 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |